From 6fb84aa3c1e1df19bf1fd303b428232c17deff98 Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Sat, 17 Feb 2024 01:00:21 -0500
Subject: [PATCH 01/19] Added JSON Data for internal citation graphs + Updated
 Graph Gen

---
 data/cleaned_complete_paper_references.json   |  2331 ++--
 .../cleaned_complete_paper_references.json    | 10395 ++++++++++++++++
 .../find_internal_reference_count.py          |   122 +-
 3 files changed, 11931 insertions(+), 917 deletions(-)
 create mode 100644 src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json

diff --git a/data/cleaned_complete_paper_references.json b/data/cleaned_complete_paper_references.json
index c90d37e..26012bb 100644
--- a/data/cleaned_complete_paper_references.json
+++ b/data/cleaned_complete_paper_references.json
@@ -2,7 +2,7 @@
     "1104d766527dead44a40532e8a89444d9cef5c65": [
         "6987c95f7054d2653178ac93df52aa3c0b99fcf5",
         "fc50a6202e2f675604543c1ae4ef22ec74f61ad5",
-        "661e8ac4908a9d2a85835245ea99b6a314cc4a60",
+        "1d490fedf59b36a397c60c6d812a4f4c8125be23",
         "7ec9e9ec1c26f7977f54dd7830d970101e3a683e",
         "bf8491bef353df126e2306ad2fe4b898697b906a",
         "87e02a265606f31e65986f3c1c448a3e3a3a066e",
@@ -26,7 +26,14 @@
     "cd29c25c489562b409a60f83365f93f33ee1a0a1": [
         "34f9c825ba24889fa5e164ba9f99bfe4fc2f3e61",
         "1104d766527dead44a40532e8a89444d9cef5c65",
-        "661e8ac4908a9d2a85835245ea99b6a314cc4a60",
+        "1d490fedf59b36a397c60c6d812a4f4c8125be23",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
+    "d4177489596748e43aa571f59556097f2cc4c8be": [
+        "6987c95f7054d2653178ac93df52aa3c0b99fcf5",
+        "db4cf9f6a653d5c15973e836c800ea47743251ae",
+        "fc50a6202e2f675604543c1ae4ef22ec74f61ad5",
+        "705e49afd92130f2bc1e0d4d0b1f6cb14e88803f",
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
     "e64df7e9448f7a9a4cb5d22c21c460134c8646ac": [
@@ -36,7 +43,7 @@
         "3e30a7ac4886b28eb50151f58e14a1d698cccd0e",
         "6987c95f7054d2653178ac93df52aa3c0b99fcf5",
         "fc50a6202e2f675604543c1ae4ef22ec74f61ad5",
-        "661e8ac4908a9d2a85835245ea99b6a314cc4a60",
+        "1d490fedf59b36a397c60c6d812a4f4c8125be23",
         "5d49c7401c5f2337c4cc88d243ae39ed659afe64"
     ],
     "fc50a6202e2f675604543c1ae4ef22ec74f61ad5": [
@@ -55,19 +62,11 @@
         "b58d8579ece27a60432e667bfbdb750590fa65d9",
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
-    "0afb64ce430c5f26752c8aed246ead6820b02049": [],
-    "0c110794ae91b4c165b0de3ff11fc841e2455bdb": [
-        "914254fac74a2da051cccf6ca16afcaad416a079",
-        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
-        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    "0afb64ce430c5f26752c8aed246ead6820b02049": [
+        "bfeb828af35c552409fcc8854418321f9afac17a",
+        "b81bb773019607a1c2fb0e956fb8ce0f49c1aaa2"
     ],
     "204fd6c5e247c477d607f507ee01d94a8dbd408f": [],
-    "243ac5656c4f8ed6e1eb757b7145fb12b837c166": [
-        "663a41c866d49ce052801fbc88947d39764cad29",
-        "cfc12c38a4d848ff3c4225488a2c72e7d4300f4b",
-        "87e02a265606f31e65986f3c1c448a3e3a3a066e",
-        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
-    ],
     "24dd96da6f700f57132713aeb5e9b06905abab5d": [],
     "2bb4fe9bc10dbf1ea70135e52452f9f63bb10671": [
         "3aaf6a2cbad5850ad81ab5c163599cb3d523436f",
@@ -92,13 +91,8 @@
         "eea7bca03bda3ee2448cd012bbcb2b33822861d8",
         "0adec918885dff698acf359988ed79a543157f80",
         "56fa0b9cba4d9aee5ccc327365b3b3a721031c69",
-        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
-    ],
-    "3b27092740a489a63589cdcf40fad6a0e093daa0": [
-        "eda54452d8a8a412c2a985ef11572cb468906b1f",
-        "70da4fb798a86cbe8cad96c27ced0415885bbd9d",
-        "bf8491bef353df126e2306ad2fe4b898697b906a",
-        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc",
+        "de04aa282f8055cebe86966c592bf37af6aecc99"
     ],
     "40c9280d87059c0cc28f2a08d46a7045fa3e9736": [
         "3aaf6a2cbad5850ad81ab5c163599cb3d523436f",
@@ -144,9 +138,13 @@
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
     "5db0f55332839c408e3049cea1a6ad48fefba70c": [
-        "17170575aa8b4fa4e3eef5d366ada706a94dd836",
         "0cfdd655100055f234fd23ebecd915504b8e00e3"
     ],
+    "64e9e1686cf85db163f007a8621e2c1b24d86feb": [
+        "914254fac74a2da051cccf6ca16afcaad416a079",
+        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
     "65fe385a665480b41fafc56d76a3bd72e92e8886": [
         "fd80f7f3673fc6ca02f192d5d73426f11a4be659",
         "cb6cc7d28d06a0d7c0d3f0d7ee551bbc86dbc3aa"
@@ -157,6 +155,12 @@
         "5f19ae1135a9500940978104ec15a5b8751bc7d2",
         "4da1cfb77084ef04a1bb6924de11eac05daa381b"
     ],
+    "705e49afd92130f2bc1e0d4d0b1f6cb14e88803f": [
+        "ac7771c332da42b29a913b116bd6ef622cbf89cf",
+        "2da3a84e72a2973504cd9cf1c0a377f5a5a91f09",
+        "4610ffb1b016acaa82a2065ffd1a3adbae1ce722",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
     "72fb75f7c38a83424308c8205bb36cd88995494b": [
         "daf9e24adbba3d1aead91cbac26502d3043db069",
         "bf8491bef353df126e2306ad2fe4b898697b906a",
@@ -166,6 +170,7 @@
     ],
     "7d87fbdfbf5038a4e0ff09801b6d3b8a2e0c613a": [],
     "8d17234680db76f99efd22fbcb169f45d2d79d93": [
+        "c85c90ef9e9a71efe031c3f7d6e34561f91168fe",
         "c589ddc6c6fb07189af7c1212f6eb15c5ff72cde",
         "c76dd4a70361c3afd2e19d046343e2dedd16ecc3",
         "4610ffb1b016acaa82a2065ffd1a3adbae1ce722",
@@ -191,6 +196,7 @@
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
     "a21de70160c91dcf9b1e7a93fbb32f4b2687860a": [
+        "3aaf6a2cbad5850ad81ab5c163599cb3d523436f",
         "bad6fa523ecf782c837a2eecaaffa4e1f7477c24",
         "c879413103f8950bdd414c7f60a39bd7748c9be8",
         "e070ff286709db28312e08b52b05539debe88146",
@@ -209,8 +215,11 @@
     "a3509cef906a4517238c1764676cf637efcd1d5e": [
         "0f733817e82026f7c29909a51cb4df7d2685f0e7"
     ],
-    "b43e9b674d4572e1aba8b40a28056ab118ad5e83": [
-        "53e7475a3ed0caee37122a9dbdb53d1da0691a33"
+    "a86dd6c62d3dc9c7989c98a3e4ace3fd8000e515": [
+        "663a41c866d49ce052801fbc88947d39764cad29",
+        "cfc12c38a4d848ff3c4225488a2c72e7d4300f4b",
+        "87e02a265606f31e65986f3c1c448a3e3a3a066e",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
     "bc70af9248d210663edf22e5fc84ca9313c697b0": [
         "097dc73d5d422b3c09286e72d16b2561ae5fb395",
@@ -219,6 +228,9 @@
     "c03fa01fbb9c77fe3d10609ba5f1dee33a723867": [
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
+    "cce17289765132b6192ccf90123bb7f5ef920c8e": [
+        "53e7475a3ed0caee37122a9dbdb53d1da0691a33"
+    ],
     "e0867e9f3a715851a90d17423f7f3b33a2a66bb1": [],
     "f64e49d76048c902cc02e8ae27dcd4ac0dbcb97f": [
         "69619a2a47faee7a29ec596db13172e2a42ff921",
@@ -257,6 +269,12 @@
         "59641c10ed7431a3cf841f308367dc2dc0281b74",
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
+    "3b27092740a489a63589cdcf40fad6a0e093daa0": [
+        "eda54452d8a8a412c2a985ef11572cb468906b1f",
+        "70da4fb798a86cbe8cad96c27ced0415885bbd9d",
+        "bf8491bef353df126e2306ad2fe4b898697b906a",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
     "49b499598a8864eee55ab264fc16a5bf8d2f87ef": [
         "0f733817e82026f7c29909a51cb4df7d2685f0e7",
         "d3640eb3b542eaf36fee2261f037a6bf0d8eac9c",
@@ -317,6 +335,7 @@
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
     "8da6e4537122af618c36563caef5863f8728d789": [
+        "04f1ff349424b4fb64a24fcaf44532d69826b0f4",
         "d3640eb3b542eaf36fee2261f037a6bf0d8eac9c",
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
@@ -325,12 +344,6 @@
         "a8e510680ecbf5ad1fa32a486b3135f9886a6c2f",
         "30873c32db5a219a58be928d5692cce48be1d3a0"
     ],
-    "9ea3d90a172a0b5799c13287484f7406946f7311": [
-        "d03a9b2a0e090cc9fd2ba0a457ecea35372f1018",
-        "13a0d8bb38f739990c8cd65a44061c6534f17221",
-        "85e7d63f75c0916bd350a229e040c5fbb1472e7a",
-        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
-    ],
     "a4929de687f3c6937dabbf733258af635781d3c4": [
         "2577d053f8aab912d29b424e1f09133d83740fd2",
         "a38e0f993e4805ba8a9beae4c275c91ffcec01df"
@@ -367,7 +380,7 @@
         "5437e8adab596d7294124c0e798708e050e25321",
         "5f19ae1135a9500940978104ec15a5b8751bc7d2",
         "23c265ba884b92ecbd9d18641078d964697e4590",
-        "166b64f2ae8e52f5779682fab756cbd617a6e74b",
+        "257aee73d83a87921fd2d56b524de394dcf6a264",
         "f9838a3be5c94bb2674a0e224de349b50e18f3c4",
         "775514b8f5a320b8772f93a3168701ad0c9eeebb"
     ],
@@ -384,7 +397,7 @@
     ],
     "0894585294c67193ff3190240554677b56fd79a0": [
         "db4cf9f6a653d5c15973e836c800ea47743251ae",
-        "661e8ac4908a9d2a85835245ea99b6a314cc4a60",
+        "1d490fedf59b36a397c60c6d812a4f4c8125be23",
         "705e49afd92130f2bc1e0d4d0b1f6cb14e88803f",
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
@@ -394,13 +407,11 @@
     ],
     "2427527c1a1bc61b32c28a107192c3e22ed629bb": [
         "fa4ea8b773ffd6706ba5cf427f9671f81b04dcdd",
-        "1104d766527dead44a40532e8a89444d9cef5c65",
         "f4d543ff431359947bf41152ac01233b8062221f",
         "286c3587f2616839286748461cbc90261ea49caf",
         "da3aca9d7b50da823f669c983edeb60445720fe0",
         "4d17732d90440682b0500f4e209c6cc4fac20e0e",
         "10bd4160b44803ada6a3d2e366c44b7e2a4ffe90",
-        "0adec918885dff698acf359988ed79a543157f80",
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
     "8c035150f883007b5af9e5bb753b78d9c0b75a55": [
@@ -426,9 +437,7 @@
         "705e49afd92130f2bc1e0d4d0b1f6cb14e88803f"
     ],
     "04892382200a9d48ad5f8d3cb3cd3d63a8206a01": [
-        "17170575aa8b4fa4e3eef5d366ada706a94dd836",
         "4610ffb1b016acaa82a2065ffd1a3adbae1ce722",
-        "07759a84f27e43cfa5bc8d579f8227c96e6ae1dc",
         "85e7d63f75c0916bd350a229e040c5fbb1472e7a"
     ],
     "07759a84f27e43cfa5bc8d579f8227c96e6ae1dc": [
@@ -445,26 +454,7 @@
         "85e7d63f75c0916bd350a229e040c5fbb1472e7a",
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
-    "0ad677b4172e5aef8b18bc6832145d1a03e11da4": [
-        "d3ca116177369bf6fbe27de64506a2f401aca996",
-        "f8a2dca1e8fe56e698984c077f7ff58d8ca867e9",
-        "b5e9406a65de7384af041c357ca5481489345b73",
-        "4780d0a027c5c5a8e01d7cf697f6296880ffc945",
-        "2f3822eb380b5e753a6d579f31dfc3ec4c4a0820",
-        "bda605928d6ebe4db906e69ab5d343df75918727",
-        "c76dd4a70361c3afd2e19d046343e2dedd16ecc3",
-        "4d81c33b295c092016ac236cfd32020a5bb70b97",
-        "4610ffb1b016acaa82a2065ffd1a3adbae1ce722",
-        "90350aa626bed47b02d0c162462e5b0ca82be6b2",
-        "07759a84f27e43cfa5bc8d579f8227c96e6ae1dc",
-        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
-        "5437e8adab596d7294124c0e798708e050e25321",
-        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
-        "87e02a265606f31e65986f3c1c448a3e3a3a066e",
-        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
-    ],
     "0da5adf32fe7501a5b98eb6549b2c42af08ee094": [],
-    "1e8403af2e1e7a8f803d8df9e8daac584f99c2a0": [],
     "294b4613b21abf1e9ba499de274569360093b107": [
         "0392d58335ce674a70f5e58ac8c438de296a0e6a",
         "0968f1592f9401d72bf0d97e740496818c1a3135",
@@ -475,10 +465,24 @@
     ],
     "2e588fe7e07948cb9112c37d5e9dcc3a13b1bd0f": [
         "0f733817e82026f7c29909a51cb4df7d2685f0e7",
-        "166b64f2ae8e52f5779682fab756cbd617a6e74b",
+        "257aee73d83a87921fd2d56b524de394dcf6a264",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
+    "3b0c49ca5ac0f441c302c9ca4def4804253552d5": [
+        "8f936af93fb2b52b9678ff8f17c1ebe8de236a88",
+        "c76dd4a70361c3afd2e19d046343e2dedd16ecc3",
+        "9af3b6b3f8dcedbb02b88936c428e1cd02503a8a",
+        "4610ffb1b016acaa82a2065ffd1a3adbae1ce722",
+        "07759a84f27e43cfa5bc8d579f8227c96e6ae1dc",
+        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
+        "cf934ddd3c852ba9c67cdfd21bf41e7723fc6d9e",
+        "f9838a3be5c94bb2674a0e224de349b50e18f3c4",
+        "ac3cdb50606f7770eef8e4cd951840a4f71287a0",
+        "59641c10ed7431a3cf841f308367dc2dc0281b74",
+        "85e7d63f75c0916bd350a229e040c5fbb1472e7a",
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
-    "3120c2763edab339b937ddbe76991ebdfe0e01e6": [],
     "515cf674fcdced5a7d5bb156dd5fcc1f5290e79b": [
         "914254fac74a2da051cccf6ca16afcaad416a079",
         "7a25155364476839b6d1fc0653cd8611327ab9ba",
@@ -488,6 +492,14 @@
         "59641c10ed7431a3cf841f308367dc2dc0281b74",
         "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
     ],
+    "7283d616e40d7ab7422e3697218f3fc42f292bf2": [
+        "f27f6d1d521d189e78f5623098ced0deea613d33",
+        "6c943670dca38bfc7c8b477ae7c2d1fba1ad3691",
+        "711d5e8ddbb840ad31a9ffa3d38590603ba69a92",
+        "e070ff286709db28312e08b52b05539debe88146",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
+        "47df3fd32d00220c85c2c51a571254fd99b2ecc7"
+    ],
     "838e1317454724a9bb758d05d97e6058e11a8251": [
         "bda605928d6ebe4db906e69ab5d343df75918727",
         "c76dd4a70361c3afd2e19d046343e2dedd16ecc3",
@@ -522,6 +534,23 @@
         "ac3cdb50606f7770eef8e4cd951840a4f71287a0",
         "85e7d63f75c0916bd350a229e040c5fbb1472e7a"
     ],
+    "cd391facabf5005419b79997b2ef8473644a8192": [
+        "d3ca116177369bf6fbe27de64506a2f401aca996",
+        "f8a2dca1e8fe56e698984c077f7ff58d8ca867e9",
+        "4780d0a027c5c5a8e01d7cf697f6296880ffc945",
+        "2f3822eb380b5e753a6d579f31dfc3ec4c4a0820",
+        "bda605928d6ebe4db906e69ab5d343df75918727",
+        "c76dd4a70361c3afd2e19d046343e2dedd16ecc3",
+        "4d81c33b295c092016ac236cfd32020a5bb70b97",
+        "4610ffb1b016acaa82a2065ffd1a3adbae1ce722",
+        "90350aa626bed47b02d0c162462e5b0ca82be6b2",
+        "07759a84f27e43cfa5bc8d579f8227c96e6ae1dc",
+        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
+        "5437e8adab596d7294124c0e798708e050e25321",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
+        "87e02a265606f31e65986f3c1c448a3e3a3a066e",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
     "d61f0820943a667917fb6d32225826aa5279f694": [
         "07759a84f27e43cfa5bc8d579f8227c96e6ae1dc",
         "e7ad08848d5d7c5c47673ffe0da06af443643bda",
@@ -572,6 +601,7 @@
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@@ -3354,6 +3203,13 @@
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@@ -3548,14 +3396,23 @@
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@@ -3648,11 +3499,9 @@
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@@ -3661,31 +3510,12 @@
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@@ -3702,6 +3532,7 @@
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@@ -3753,20 +3591,15 @@
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@@ -3789,18 +3622,21 @@
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@@ -3840,6 +3676,7 @@
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@@ -4240,8 +4144,23 @@
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@@ -4263,6 +4182,14 @@
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@@ -4387,7 +4321,13 @@
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@@ -4398,12 +4338,6 @@
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@@ -4449,6 +4383,7 @@
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@@ -4542,12 +4483,6 @@
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@@ -7131,21 +7050,6 @@
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@@ -7189,13 +7093,28 @@
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@@ -7261,19 +7180,6 @@
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@@ -7413,7 +7319,7 @@
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@@ -8381,7 +8273,15 @@
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@@ -9182,10 +9116,6 @@
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@@ -9424,10 +9667,116 @@
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@@ -9756,7 +10318,8 @@
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diff --git a/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json b/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json
new file mode 100644
index 0000000..26012bb
--- /dev/null
+++ b/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json
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+    ],
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+    ],
+    "ef5f7cd21b5d34797636239a7b9c8ba6af440aab": [
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+        "5437e8adab596d7294124c0e798708e050e25321"
+    ],
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+        "5437e8adab596d7294124c0e798708e050e25321",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
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+        "a38e0f993e4805ba8a9beae4c275c91ffcec01df",
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+    ],
+    "b115c1e1e9e51f8ad7d47b745bc04e29a654b84d": [
+        "7dc928f41e15f65f1267bd87b0fcfcc7e715cb56",
+        "dca6c3927ade6481a1ae080f5c24decbfeced1be",
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+        "5437e8adab596d7294124c0e798708e050e25321",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
+    "3d473cbb7a377cf960abff31748a1a39bb6c7d7c": [
+        "aade40af0d85b0b4fe15c97f6222d5c2e4d6d9b3",
+        "507acddb0b7f36b83fd7c8bff2f121eb506ac8fb",
+        "2f3822eb380b5e753a6d579f31dfc3ec4c4a0820",
+        "6c943670dca38bfc7c8b477ae7c2d1fba1ad3691",
+        "e070ff286709db28312e08b52b05539debe88146",
+        "e826ac71dad8c4ce36d82fb7add43e3d306bb7e1",
+        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
+    "5f19ae1135a9500940978104ec15a5b8751bc7d2": [
+        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
+        "9ffefdf1fcd780cb71450b0a7a29247c66aa87be",
+        "0adec918885dff698acf359988ed79a543157f80",
+        "56fa0b9cba4d9aee5ccc327365b3b3a721031c69",
+        "85e7d63f75c0916bd350a229e040c5fbb1472e7a",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc",
+        "de04aa282f8055cebe86966c592bf37af6aecc99"
+    ],
+    "5f66d1a667eec13b5d337c3fc5619bcef95092bd": [
+        "f8a2dca1e8fe56e698984c077f7ff58d8ca867e9",
+        "815c6ca281536d18ec0eb408b6e46e72a0826163",
+        "e826ac71dad8c4ce36d82fb7add43e3d306bb7e1",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
+        "a38e0f993e4805ba8a9beae4c275c91ffcec01df"
+    ],
+    "142ebbf4760145f591166bde2564ac70c001e927": [
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2"
+    ],
+    "3aaf6a2cbad5850ad81ab5c163599cb3d523436f": [
+        "e5c72b92c48d68594b290c84a8904da7c8335554",
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+        "6c1e1cc1e0e1f8fd026fe517607b2d4535565fa7",
+        "e070ff286709db28312e08b52b05539debe88146",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
+    "7715ba5e75f5256e1061c7473afe61bb0dbb9065": [
+        "29bd550d0ab53296790ceba31dfe0a06754bcdde",
+        "6c1e1cc1e0e1f8fd026fe517607b2d4535565fa7",
+        "90350aa626bed47b02d0c162462e5b0ca82be6b2",
+        "c88cafa3e980765a64febe369ceb7c2aa7261d2a",
+        "baf63d7cf115d674a8c8da3a3d789aa84521977a",
+        "5437e8adab596d7294124c0e798708e050e25321",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
+    "8236010c2ecc94d826be6010ff187fdc000e7df6": [
+        "3aaf6a2cbad5850ad81ab5c163599cb3d523436f",
+        "a3a241e9397fe29b37f96cb5e8f4b8bebed3d3da",
+        "3d68522abfadfc8ee6b7ec9edaaf91f1b2f38e5e",
+        "b115c1e1e9e51f8ad7d47b745bc04e29a654b84d",
+        "6c943670dca38bfc7c8b477ae7c2d1fba1ad3691",
+        "4d17732d90440682b0500f4e209c6cc4fac20e0e",
+        "711d5e8ddbb840ad31a9ffa3d38590603ba69a92",
+        "90350aa626bed47b02d0c162462e5b0ca82be6b2",
+        "40047a74b707743157051d38f76061ba5ff9aab4",
+        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
+        "5437e8adab596d7294124c0e798708e050e25321",
+        "13a0d8bb38f739990c8cd65a44061c6534f17221",
+        "341bdbcfc3febef7691a97c216ad394653211095",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
+    "4b0b56be0ae9479d2bd5c2f0943db1906343c10f": [
+        "8236010c2ecc94d826be6010ff187fdc000e7df6",
+        "3aaf6a2cbad5850ad81ab5c163599cb3d523436f",
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+        "e5c72b92c48d68594b290c84a8904da7c8335554",
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+        "e070ff286709db28312e08b52b05539debe88146",
+        "142ebbf4760145f591166bde2564ac70c001e927",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2"
+    ],
+    "50b0c6ee2b3d53ba5af69d6c00b5d60888a9026f": [
+        "341bdbcfc3febef7691a97c216ad394653211095",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
+        "521ccc898395a2818fced22b4cf371b0e5121f94",
+        "0adec918885dff698acf359988ed79a543157f80",
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+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc",
+        "de04aa282f8055cebe86966c592bf37af6aecc99"
+    ],
+    "01286de359fa9dd3d8f78c48157a3e929533d94e": [
+        "2f3822eb380b5e753a6d579f31dfc3ec4c4a0820",
+        "30c0cdc414f68211d5d0514df027cec22e005174",
+        "4610ffb1b016acaa82a2065ffd1a3adbae1ce722",
+        "663a41c866d49ce052801fbc88947d39764cad29",
+        "de32da8f5c6a50a6c311e9357ba16aa7d05a1bc9",
+        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
+        "4e5f7cd537a1bbcd090f9887b1b59f39a3715dba"
+    ],
+    "e7c85d7d58d4b1fde4be8a8f166e46c995dc0f1b": [
+        "aade40af0d85b0b4fe15c97f6222d5c2e4d6d9b3",
+        "4780d0a027c5c5a8e01d7cf697f6296880ffc945",
+        "2f3822eb380b5e753a6d579f31dfc3ec4c4a0820",
+        "7dc928f41e15f65f1267bd87b0fcfcc7e715cb56",
+        "62176de125738e3b95850d1227bac81fd646b78e",
+        "ccc772d88c231275f24c4fac9b28bbe0942e1107",
+        "5f5253fb15ac382e96ade0335baf1cfaa240fb1d",
+        "b115c1e1e9e51f8ad7d47b745bc04e29a654b84d",
+        "6c1e1cc1e0e1f8fd026fe517607b2d4535565fa7",
+        "663a41c866d49ce052801fbc88947d39764cad29",
+        "c88cafa3e980765a64febe369ceb7c2aa7261d2a",
+        "e826ac71dad8c4ce36d82fb7add43e3d306bb7e1",
+        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
+        "5437e8adab596d7294124c0e798708e050e25321",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
+    "0aa150619e07fa41492517368beaaf8ae56fe061": [
+        "3d68522abfadfc8ee6b7ec9edaaf91f1b2f38e5e",
+        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2"
+    ],
+    "717392dac099d1506b766787382d61b277863163": [
+        "90350aa626bed47b02d0c162462e5b0ca82be6b2",
+        "4988b3d378b79eb8669112620baf1ff4e3e536fd",
+        "07759a84f27e43cfa5bc8d579f8227c96e6ae1dc",
+        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
+        "f9838a3be5c94bb2674a0e224de349b50e18f3c4",
+        "59641c10ed7431a3cf841f308367dc2dc0281b74",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ],
+    "850538c1759c56a9f2dab8e84ec63801c41d6396": [
+        "5aa834e8088818e6ba138d5a1b7b66f69d619555",
+        "4b0b56be0ae9479d2bd5c2f0943db1906343c10f",
+        "3aaf6a2cbad5850ad81ab5c163599cb3d523436f",
+        "3d68522abfadfc8ee6b7ec9edaaf91f1b2f38e5e",
+        "e070ff286709db28312e08b52b05539debe88146",
+        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
+        "5437e8adab596d7294124c0e798708e050e25321"
+    ],
+    "5aa834e8088818e6ba138d5a1b7b66f69d619555": [
+        "ed40889e11e812ef33578506844be06d713f6092",
+        "2f3822eb380b5e753a6d579f31dfc3ec4c4a0820",
+        "7dc928f41e15f65f1267bd87b0fcfcc7e715cb56",
+        "c76dd4a70361c3afd2e19d046343e2dedd16ecc3",
+        "385376b8aa48c25403f17d6206db7c09b67e1314",
+        "3aaf6a2cbad5850ad81ab5c163599cb3d523436f",
+        "08b85bce712168998004ee80ce4e475390413c74",
+        "69619a2a47faee7a29ec596db13172e2a42ff921",
+        "4610ffb1b016acaa82a2065ffd1a3adbae1ce722",
+        "66242baf48b0f6b828e7547ac39ffaa5e1b2cb3e",
+        "e070ff286709db28312e08b52b05539debe88146",
+        "50b0c6ee2b3d53ba5af69d6c00b5d60888a9026f",
+        "e7ad08848d5d7c5c47673ffe0da06af443643bda",
+        "5437e8adab596d7294124c0e798708e050e25321",
+        "5f19ae1135a9500940978104ec15a5b8751bc7d2",
+        "d53e70d834243d3d8d4b621c0c52dfec26081155",
+        "ac3cdb50606f7770eef8e4cd951840a4f71287a0",
+        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
+    ]
+}
\ No newline at end of file
diff --git a/src/prompt_systematic_review/experiments/find_internal_reference_count.py b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
index eee456e..9f7f888 100644
--- a/src/prompt_systematic_review/experiments/find_internal_reference_count.py
+++ b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
@@ -109,7 +109,7 @@ def get_paper_title(paper_id, api_key):
 
 
 # Path to the CSV file containing the papers' data
-csv_file_path = "master_papers.csv"
+csv_file_path = "./data/master_papers.csv"
 
 # Dictionary to hold the references
 paper_references = {}
@@ -242,7 +242,9 @@ def extract_arxiv_id(url):
 
 # Processing the papers with a progress bar
 for title in tqdm(titles):
-    paper_id, references = get_references(title, api_key)
+    paper_id = query_paper_id(title, api_key)
+    references = get_references(paper_id, api_key)
+
     if paper_id:
         paper_references[paper_id] = references
     else:
@@ -381,13 +383,14 @@ def extract_arxiv_id(url):
 
 titles_above_threshold = {}
 for paper_id in top_nodes:
-    title = get_paper_title(paper_id, api_key)
-    if title:
-        if len(title) > 50:
-            title = textwrap.shorten(
-                title, width=50, placeholder="..."
+    full_title = get_paper_title(paper_id, api_key)
+    if full_title:
+        display_title = title_mappings.get(full_title, full_title)  # Use the full title if not found in the dictionary
+        if len(display_title) > 50:
+            display_title = textwrap.shorten(
+                display_title, width=50, placeholder="..."
             )  # Shorten if longer than 60s
-        titles_above_threshold[paper_id] = title
+        titles_above_threshold[paper_id] = display_title
 
 # Cap the maximum node size to prevent too large nodes
 # max_size = 100000  # Maximum size for a node
@@ -423,31 +426,49 @@ def extract_arxiv_id(url):
 import textwrap
 
 
-def adjust_overlap(pos, nodes_to_adjust, min_dist=0.1, repulsion_factor=1.05):
+def adjust_overlap(pos, nodes_to_adjust, min_dist=1.0, repulsion_factor=1.05, vertical_bias=2.0):
     for _ in range(1000):  # Increase the number of iterations for a denser graph
         adjusted = False
-        for i, node1 in enumerate(nodes_to_adjust):
-            for node2 in nodes_to_adjust[i + 1 :]:
+        for node1 in nodes_to_adjust:
+            for node2 in nodes_to_adjust:
+                if node1 == node2:
+                    continue  # Skip comparing the node to itself
                 x1, y1 = pos[node1]
                 x2, y2 = pos[node2]
                 dx, dy = x1 - x2, y1 - y2
-                dist = (dx**2 + dy**2) ** 0.5
+                dist = (dx**2 + dy**2)**0.5
                 if dist < min_dist:  # If nodes are too close, push them apart
-                    # Apply a repulsion factor to move nodes further apart
                     if dist == 0:  # To avoid division by zero
                         dx, dy = 1, 0
                         dist = 1
-                    dx, dy = (
-                        dx / dist * min_dist * repulsion_factor,
-                        dy / dist * min_dist * repulsion_factor,
-                    )
-                    pos[node1] = (x1 + dx, y1 + dy)
-                    pos[node2] = (x2 - dx, y2 - dy)
-                    adjusted = True
+                    dx, dy = dx / dist * min_dist * repulsion_factor, dy / dist * min_dist * repulsion_factor
+
+                    # Apply additional vertical adjustment
+                    dy *= vertical_bias
+
+                    # Update the position of node1
+                    new_x1, new_y1 = x1 + dx, y1 + dy
+
+                    # Check if the new position of node1 overlaps with other nodes
+                    overlap = False
+                    for other_node in nodes_to_adjust:
+                        if other_node == node1 or other_node == node2:
+                            continue
+                        ox, oy = pos[other_node]
+                        if ((new_x1 - ox)**2 + (new_y1 - oy)**2)**0.5 < min_dist:
+                            overlap = True
+                            break
+
+                    if not overlap:
+                        pos[node1] = new_x1, new_y1
+                        adjusted = True
+
         if not adjusted:  # Break the loop if no adjustments were made
             break
 
 
+
+
 # Load the cleaned references
 with open("cleaned_complete_paper_references.json", "r") as json_file:
     paper_references = json.load(json_file)
@@ -457,6 +478,35 @@ def adjust_overlap(pos, nodes_to_adjust, min_dist=0.1, repulsion_factor=1.05):
 for paper_id, references in paper_references.items():
     for ref_id in references:
         G.add_edge(paper_id, ref_id)
+        
+title_to_technique = {
+    "Language Models are Few-Shot Learners": "In-Context Learning (ICL)",
+    "Calibrate Before Use: Improving Few-Shot Performance of Language Models": "Calibration for FSL",
+    "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity": "Example Ordering",
+    "What Makes Good In-Context Examples for GPT-3?": "Good Example Criteria",
+    "Making Pre-trained Language Models Better Few-shot Learners": "Pre-trained FSL Improvement",
+    "Self-Consistency Improves Chain of Thought Reasoning in Language Models": "Self-Consistency",
+    "Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?": "Example Label Quality",
+    "Large Language Models are Zero-Shot Reasoners": "Zero-Shot-CoT",
+    "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models": "Least-to-Most Prompting",
+    "True Few-Shot Learning with Language Models": "True FSL",
+    "Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm": "Role Prompting",
+    "Learning To Retrieve Prompts for In-Context Learning": "Prompt Retrieval Learning",
+    "An Explanation of In-context Learning as Implicit Bayesian Inference": "Implicit Bayesian Inference",
+    "OPT: Open Pre-trained Transformer Language Models": "Open Pre-trained Transformer (OPT)",
+    "Do Prompt-Based Models Really Understand the Meaning of Their Prompts?": "Prompt Meaning Understanding",
+    "MetaICL: Learning to Learn In Context": "Meta In-Context Learning (MetaICL)",
+    "Noisy Channel Language Model Prompting for Few-Shot Text Classification": "Noisy Channel Prompting",
+    "Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them": "BIG-Bench & CoT",
+    "Can language models learn from explanations in context?": "Learning from Explanations",
+    "Reframing Instructional Prompts to GPTk’s Language": "Instructional Prompt Reframing",
+    "Meta-learning via Language Model In-context Tuning": "Meta-Learning LM Tuning",
+    "Program Synthesis with Large Language Models": "Program Synthesis LLM",
+    "Selective Annotation Makes Language Models Better Few-Shot Learners": "Input Distribution",
+    "Measuring and Narrowing the Compositionality Gap in Language Models": "Compositionality Gap",
+    "Rationale-Augmented Ensembles in Language Models": "Rationale-Augmented Ensembles",
+}
+
 
 # Remove isolated nodes and nodes with less than 10 incoming edges
 G.remove_nodes_from(list(nx.isolates(G)))
@@ -468,7 +518,7 @@ def adjust_overlap(pos, nodes_to_adjust, min_dist=0.1, repulsion_factor=1.05):
 
 
 # Define a function to wrap text into at most two lines
-def wrap_text(text, width, max_lines=2):
+def wrap_text(text, width, max_lines=3):
     wrapped_lines = textwrap.wrap(text, width)
     if len(wrapped_lines) > max_lines:
         wrapped_lines = wrapped_lines[:max_lines]
@@ -478,19 +528,23 @@ def wrap_text(text, width, max_lines=2):
     return "\n".join(wrapped_lines)
 
 
+# Adjusted part of your code for assigning and labeling top nodes with titles
 titles_above_threshold = {}
 for paper_id in top_nodes:
-    title = get_paper_title(
-        paper_id, api_key
-    )  # Function to fetch paper title using paper_id
-    if title:
-        wrapped_title = wrap_text(title, 40)  # Wrap the title into at most two lines
+    full_title = get_paper_title(paper_id, api_key)  # Fetch full paper title
+    if full_title:
+        # Check if the full title is in your dictionary and use the mapped value if it is
+        display_title = title_to_technique.get(full_title, full_title)  # Use the full title if not found in the dictionary
+        wrapped_title = wrap_text(display_title, 16)  # Wrap the title (or its mapped value)
         titles_above_threshold[paper_id] = wrapped_title
 
 
 # Cap the maximum node size to prevent too large nodes
 node_sizes = [G.in_degree(node) * 2000 for node in G.nodes()]
 
+# Calculate font size based on in-degree (you can adjust the scaling factor)
+font_sizes = {node: G.in_degree(node) * .2 + 14 for node in G.nodes()}  # '+ 10' ensures a minimum font size
+
 # Draw the graph with adjusted layout parameters
 plt.figure(figsize=(60, 35))
 pos = nx.kamada_kawai_layout(G, dist=None, scale=1)
@@ -498,19 +552,19 @@ def wrap_text(text, width, max_lines=2):
 adjust_overlap(pos, top_nodes, min_dist=0.2, repulsion_factor=1.05)
 
 # Draw all nodes first
-nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color="skyblue")
+nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color=(45 / 255, 137 / 255, 145 / 255, 1))
 
 # Draw the edges
-nx.draw_networkx_edges(G, pos, edge_color="gray", width=0.5)
+nx.draw_networkx_edges(G, pos, edge_color='gray', width=0.5)
 
 # Assign and label top nodes with titles
 for node, label in titles_above_threshold.items():
     x, y = pos[node]
-    num_lines = label.count("\n") + 1
-    y_offset = 0.01 * 2  # Adjust this factor as needed to position the text correctly
-    plt.text(x, y + y_offset, label, fontsize=20, ha="center", va="center")
+    num_lines = label.count('\n') + 1
+    y_offset = 0.005 * num_lines  # Adjust this factor as needed to position the text correctly
+    plt.text(x, y + y_offset, label, fontsize=font_sizes[node], ha='center', va='center')
 
-plt.title("Graph of Paper References")
+plt.title("Directed Graph of Paper's Internal References", fontsize=50)
 plt.show()
 
 # %%
@@ -612,7 +666,7 @@ def wrap_text(text, width, max_lines=2):
 )  # RGBA color
 
 # Rotate the x-axis labels by 45 degrees for better readability
-plt.xticks(rotation=90, ha="right")  # ha='right' aligns the labels at the right angle
+plt.xticks(rotation=45, ha="right")  # ha='right' aligns the labels at the right angle
 
 # Add labels and title
 plt.ylabel("Number of References")
@@ -621,3 +675,5 @@ def wrap_text(text, width, max_lines=2):
 
 plt.tight_layout()  # Adjusts layout to prevent clipping of labels
 plt.show()
+
+# %%

From e907eb32c529ff779584866e0e1de3ff8e836876 Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Sat, 17 Feb 2024 01:03:26 -0500
Subject: [PATCH 02/19] Moved JSON into data

---
 .../cleaned_complete_paper_references.json    | 10395 ----------------
 .../find_internal_reference_count.py          |    49 +-
 2 files changed, 33 insertions(+), 10411 deletions(-)
 delete mode 100644 src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json

diff --git a/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json b/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json
deleted file mode 100644
index 26012bb..0000000
--- a/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json
+++ /dev/null
@@ -1,10395 +0,0 @@
-{
-    "1104d766527dead44a40532e8a89444d9cef5c65": [
-        "6987c95f7054d2653178ac93df52aa3c0b99fcf5",
-        "fc50a6202e2f675604543c1ae4ef22ec74f61ad5",
-        "1d490fedf59b36a397c60c6d812a4f4c8125be23",
-        "7ec9e9ec1c26f7977f54dd7830d970101e3a683e",
-        "bf8491bef353df126e2306ad2fe4b898697b906a",
-        "87e02a265606f31e65986f3c1c448a3e3a3a066e",
-        "8fdd34153d1035d09dd4a6efa9cb0c91d23d0045"
-    ],
-    "3c784cd3150a359e269c70cfbadd18774d66055d": [
-        "fc50a6202e2f675604543c1ae4ef22ec74f61ad5",
-        "6bd91a3183ddb844641acb9f3fe9faec6a9ff617"
-    ],
-    "3e30a7ac4886b28eb50151f58e14a1d698cccd0e": [
-        "19443d48399d4fe89a4b0a96917c50c6fd9c5af1",
-        "1104d766527dead44a40532e8a89444d9cef5c65",
-        "cb6cc7d28d06a0d7c0d3f0d7ee551bbc86dbc3aa",
-        "705e49afd92130f2bc1e0d4d0b1f6cb14e88803f"
-    ],
-    "ace98e1e58bcc364afbb2feff6d136232f5f47da": [
-        "fc50a6202e2f675604543c1ae4ef22ec74f61ad5",
-        "705e49afd92130f2bc1e0d4d0b1f6cb14e88803f",
-        "2afb07359e9c67499e1f373ac6f1520d3ea9c46a"
-    ],
-    "cd29c25c489562b409a60f83365f93f33ee1a0a1": [
-        "34f9c825ba24889fa5e164ba9f99bfe4fc2f3e61",
-        "1104d766527dead44a40532e8a89444d9cef5c65",
-        "1d490fedf59b36a397c60c6d812a4f4c8125be23",
-        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
-    ],
-    "d4177489596748e43aa571f59556097f2cc4c8be": [
-        "6987c95f7054d2653178ac93df52aa3c0b99fcf5",
-        "db4cf9f6a653d5c15973e836c800ea47743251ae",
-        "fc50a6202e2f675604543c1ae4ef22ec74f61ad5",
-        "705e49afd92130f2bc1e0d4d0b1f6cb14e88803f",
-        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
-    ],
-    "e64df7e9448f7a9a4cb5d22c21c460134c8646ac": [
-        "6b85b63579a916f705a8e10a49bd8d849d91b1fc"
-    ],
-    "f3f23f7f9f5369aade19f20bc5d028cce7b9c9aa": [
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-        "5d49c7401c5f2337c4cc88d243ae39ed659afe64"
-    ],
-    "fc50a6202e2f675604543c1ae4ef22ec74f61ad5": [
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-        "08b85bce712168998004ee80ce4e475390413c74",
-        "c879413103f8950bdd414c7f60a39bd7748c9be8",
-        "ac3cdb50606f7770eef8e4cd951840a4f71287a0"
-    ],
-    "07955e96cbd778d0ae2a68f09d073b866dd84c2a": [
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-    ]
-}
\ No newline at end of file
diff --git a/src/prompt_systematic_review/experiments/find_internal_reference_count.py b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
index 9f7f888..9cfe7a3 100644
--- a/src/prompt_systematic_review/experiments/find_internal_reference_count.py
+++ b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
@@ -385,7 +385,9 @@ def extract_arxiv_id(url):
 for paper_id in top_nodes:
     full_title = get_paper_title(paper_id, api_key)
     if full_title:
-        display_title = title_mappings.get(full_title, full_title)  # Use the full title if not found in the dictionary
+        display_title = title_mappings.get(
+            full_title, full_title
+        )  # Use the full title if not found in the dictionary
         if len(display_title) > 50:
             display_title = textwrap.shorten(
                 display_title, width=50, placeholder="..."
@@ -426,7 +428,9 @@ def extract_arxiv_id(url):
 import textwrap
 
 
-def adjust_overlap(pos, nodes_to_adjust, min_dist=1.0, repulsion_factor=1.05, vertical_bias=2.0):
+def adjust_overlap(
+    pos, nodes_to_adjust, min_dist=1.0, repulsion_factor=1.05, vertical_bias=2.0
+):
     for _ in range(1000):  # Increase the number of iterations for a denser graph
         adjusted = False
         for node1 in nodes_to_adjust:
@@ -436,12 +440,15 @@ def adjust_overlap(pos, nodes_to_adjust, min_dist=1.0, repulsion_factor=1.05, ve
                 x1, y1 = pos[node1]
                 x2, y2 = pos[node2]
                 dx, dy = x1 - x2, y1 - y2
-                dist = (dx**2 + dy**2)**0.5
+                dist = (dx**2 + dy**2) ** 0.5
                 if dist < min_dist:  # If nodes are too close, push them apart
                     if dist == 0:  # To avoid division by zero
                         dx, dy = 1, 0
                         dist = 1
-                    dx, dy = dx / dist * min_dist * repulsion_factor, dy / dist * min_dist * repulsion_factor
+                    dx, dy = (
+                        dx / dist * min_dist * repulsion_factor,
+                        dy / dist * min_dist * repulsion_factor,
+                    )
 
                     # Apply additional vertical adjustment
                     dy *= vertical_bias
@@ -455,7 +462,7 @@ def adjust_overlap(pos, nodes_to_adjust, min_dist=1.0, repulsion_factor=1.05, ve
                         if other_node == node1 or other_node == node2:
                             continue
                         ox, oy = pos[other_node]
-                        if ((new_x1 - ox)**2 + (new_y1 - oy)**2)**0.5 < min_dist:
+                        if ((new_x1 - ox) ** 2 + (new_y1 - oy) ** 2) ** 0.5 < min_dist:
                             overlap = True
                             break
 
@@ -467,8 +474,6 @@ def adjust_overlap(pos, nodes_to_adjust, min_dist=1.0, repulsion_factor=1.05, ve
             break
 
 
-
-
 # Load the cleaned references
 with open("cleaned_complete_paper_references.json", "r") as json_file:
     paper_references = json.load(json_file)
@@ -478,7 +483,7 @@ def adjust_overlap(pos, nodes_to_adjust, min_dist=1.0, repulsion_factor=1.05, ve
 for paper_id, references in paper_references.items():
     for ref_id in references:
         G.add_edge(paper_id, ref_id)
-        
+
 title_to_technique = {
     "Language Models are Few-Shot Learners": "In-Context Learning (ICL)",
     "Calibrate Before Use: Improving Few-Shot Performance of Language Models": "Calibration for FSL",
@@ -534,8 +539,12 @@ def wrap_text(text, width, max_lines=3):
     full_title = get_paper_title(paper_id, api_key)  # Fetch full paper title
     if full_title:
         # Check if the full title is in your dictionary and use the mapped value if it is
-        display_title = title_to_technique.get(full_title, full_title)  # Use the full title if not found in the dictionary
-        wrapped_title = wrap_text(display_title, 16)  # Wrap the title (or its mapped value)
+        display_title = title_to_technique.get(
+            full_title, full_title
+        )  # Use the full title if not found in the dictionary
+        wrapped_title = wrap_text(
+            display_title, 16
+        )  # Wrap the title (or its mapped value)
         titles_above_threshold[paper_id] = wrapped_title
 
 
@@ -543,7 +552,9 @@ def wrap_text(text, width, max_lines=3):
 node_sizes = [G.in_degree(node) * 2000 for node in G.nodes()]
 
 # Calculate font size based on in-degree (you can adjust the scaling factor)
-font_sizes = {node: G.in_degree(node) * .2 + 14 for node in G.nodes()}  # '+ 10' ensures a minimum font size
+font_sizes = {
+    node: G.in_degree(node) * 0.2 + 14 for node in G.nodes()
+}  # '+ 10' ensures a minimum font size
 
 # Draw the graph with adjusted layout parameters
 plt.figure(figsize=(60, 35))
@@ -552,17 +563,23 @@ def wrap_text(text, width, max_lines=3):
 adjust_overlap(pos, top_nodes, min_dist=0.2, repulsion_factor=1.05)
 
 # Draw all nodes first
-nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color=(45 / 255, 137 / 255, 145 / 255, 1))
+nx.draw_networkx_nodes(
+    G, pos, node_size=node_sizes, node_color=(45 / 255, 137 / 255, 145 / 255, 1)
+)
 
 # Draw the edges
-nx.draw_networkx_edges(G, pos, edge_color='gray', width=0.5)
+nx.draw_networkx_edges(G, pos, edge_color="gray", width=0.5)
 
 # Assign and label top nodes with titles
 for node, label in titles_above_threshold.items():
     x, y = pos[node]
-    num_lines = label.count('\n') + 1
-    y_offset = 0.005 * num_lines  # Adjust this factor as needed to position the text correctly
-    plt.text(x, y + y_offset, label, fontsize=font_sizes[node], ha='center', va='center')
+    num_lines = label.count("\n") + 1
+    y_offset = (
+        0.005 * num_lines
+    )  # Adjust this factor as needed to position the text correctly
+    plt.text(
+        x, y + y_offset, label, fontsize=font_sizes[node], ha="center", va="center"
+    )
 
 plt.title("Directed Graph of Paper's Internal References", fontsize=50)
 plt.show()

From 2e5864c38697b8fca59fab4b28575a22aac1c0b9 Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Sat, 17 Feb 2024 01:06:10 -0500
Subject: [PATCH 03/19] Updated References to JSON

---
 .../experiments/find_internal_reference_count.py       | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/prompt_systematic_review/experiments/find_internal_reference_count.py b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
index 9cfe7a3..498cf86 100644
--- a/src/prompt_systematic_review/experiments/find_internal_reference_count.py
+++ b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
@@ -296,7 +296,7 @@ def extract_arxiv_id(url):
     ]
 
 # Save the cleaned data back to a JSON file
-with open("cleaned_complete_paper_references.json", "w") as file:
+with open("data/cleaned_complete_paper_references.json", "w") as file:
     json.dump(merged_paper_references, file, indent=4)
 
 print("Cleaned data saved to cleaned_merged_paper_references.json")
@@ -319,7 +319,7 @@ def extract_arxiv_id(url):
 )
 
 # Load the data
-with open("cleaned_complete_paper_references.json", "r") as file:
+with open("data/cleaned_complete_paper_references.json", "r") as file:
     data = json.load(file)
 
 # Count the number of references for each key
@@ -364,7 +364,7 @@ def extract_arxiv_id(url):
 import textwrap
 
 # Load the cleaned references
-with open("cleaned_complete_paper_references.json", "r") as json_file:
+with open("data/cleaned_complete_paper_references.json", "r") as json_file:
     paper_references = json.load(json_file)
 
 # Create the graph
@@ -475,7 +475,7 @@ def adjust_overlap(
 
 
 # Load the cleaned references
-with open("cleaned_complete_paper_references.json", "r") as json_file:
+with open("data/cleaned_complete_paper_references.json", "r") as json_file:
     paper_references = json.load(json_file)
 
 # Create the graph
@@ -658,7 +658,7 @@ def wrap_text(text, width, max_lines=3):
 }
 
 # Load the existing dictionary of paper references
-with open("cleaned_complete_paper_references.json", "r") as file:
+with open("data/cleaned_complete_paper_references.json", "r") as file:
     paper_references = json.load(file)
 
 # Query each title and get citation counts

From 2608072c1be7cf68f2f94ffbb854ea8c63b9c795 Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Mon, 19 Feb 2024 00:31:05 -0500
Subject: [PATCH 04/19] Latest Updaates

---
 .../find_internal_reference_count.py          | 66 ++++++++++++-------
 1 file changed, 41 insertions(+), 25 deletions(-)

diff --git a/src/prompt_systematic_review/experiments/find_internal_reference_count.py b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
index 498cf86..5e365a9 100644
--- a/src/prompt_systematic_review/experiments/find_internal_reference_count.py
+++ b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
@@ -475,7 +475,7 @@ def adjust_overlap(
 
 
 # Load the cleaned references
-with open("data/cleaned_complete_paper_references.json", "r") as json_file:
+with open("/Users/aayushgupta/Documents/GitHub/Prompt_Systematic_Review/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json", "r") as json_file:
     paper_references = json.load(json_file)
 
 # Create the graph
@@ -485,17 +485,17 @@ def adjust_overlap(
         G.add_edge(paper_id, ref_id)
 
 title_to_technique = {
-    "Language Models are Few-Shot Learners": "In-Context Learning (ICL)",
+    "Language Models are Few-Shot Learners": "Few-Shot Prompting",
     "Calibrate Before Use: Improving Few-Shot Performance of Language Models": "Calibration for FSL",
     "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity": "Example Ordering",
-    "What Makes Good In-Context Examples for GPT-3?": "Good Example Criteria",
+    "What Makes Good In-Context Examples for GPT-3?": "In-Context Learning (ICL)",
     "Making Pre-trained Language Models Better Few-shot Learners": "Pre-trained FSL Improvement",
     "Self-Consistency Improves Chain of Thought Reasoning in Language Models": "Self-Consistency",
     "Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?": "Example Label Quality",
     "Large Language Models are Zero-Shot Reasoners": "Zero-Shot-CoT",
     "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models": "Least-to-Most Prompting",
     "True Few-Shot Learning with Language Models": "True FSL",
-    "Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm": "Role Prompting",
+    "Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm": "Beyond the Few-Shot Paradigm",
     "Learning To Retrieve Prompts for In-Context Learning": "Prompt Retrieval Learning",
     "An Explanation of In-context Learning as Implicit Bayesian Inference": "Implicit Bayesian Inference",
     "OPT: Open Pre-trained Transformer Language Models": "Open Pre-trained Transformer (OPT)",
@@ -513,14 +513,24 @@ def adjust_overlap(
 }
 
 
-# Remove isolated nodes and nodes with less than 10 incoming edges
-G.remove_nodes_from(list(nx.isolates(G)))
-nodes_to_remove = [node for node in G.nodes() if G.in_degree(node) < 8]
-G.remove_nodes_from(nodes_to_remove)
+
 
 # Find the top 20 nodes with the most incoming edges
 top_nodes = sorted(G.nodes(), key=lambda n: G.in_degree(n), reverse=True)[:25]
 
+# Print the name of the paper and the amount of references for the top 20 nodes
+print("Top 20 Referenced Papers:")
+for paper_id in top_nodes:
+    in_degree = G.in_degree(paper_id)  # The number of references
+    full_title = get_paper_title(paper_id, api_key)  # Assuming this function fetches the paper title
+    display_title = title_to_technique.get(full_title, full_title)  # Use mapped value if exists, otherwise full title
+    print(f'"{display_title}" referenced by {in_degree} other papers and its paper_id was {paper_id}')
+
+
+# Remove isolated nodes and nodes with less than 10 incoming edges
+G.remove_nodes_from(list(nx.isolates(G)))
+nodes_to_remove = [node for node in G.nodes() if G.in_degree(node) < 8]
+G.remove_nodes_from(nodes_to_remove)
 
 # Define a function to wrap text into at most two lines
 def wrap_text(text, width, max_lines=3):
@@ -580,8 +590,9 @@ def wrap_text(text, width, max_lines=3):
     plt.text(
         x, y + y_offset, label, fontsize=font_sizes[node], ha="center", va="center"
     )
+plt.axis('off')
 
-plt.title("Directed Graph of Paper's Internal References", fontsize=50)
+# plt.title("Directed Graph of Paper's Internal References", fontsize=50)
 plt.show()
 
 # %%
@@ -598,9 +609,9 @@ def wrap_text(text, width, max_lines=3):
     "A Practical Survey on Zero-Shot Prompt Design for In-Context Learning": "Zero-Shot Prompt",
     "Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm": "Role Prompting",
     "Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints": "Style Prompting",
-    "Language Models are Few-Shot Learners": "In-context learning (ICL)",
-    "A Survey on In-context Learning": "Few-shot learning (FSL)",
-    "What Makes Good In-Context Examples for {GPT}-3?": "K-Nearest Neighbor (KNN)",
+    "Language Models are Few-Shot Learners": "Few-shot Learning (FSL)",
+    # "A Survey on In-context Learning": "In-context learning (ICL)",
+    "What Makes Good In-Context Examples for {\GPT}-3?": "In-Context Learning (ICL)",
     "Finding Support Examples for In-Context Learning": "fiLter-thEN-Search (LENS)",
     "Unified Demonstration Retriever for In-Context Learning": "Unified Demonstration Retriever (UDR)",
     "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity": "Example Ordering",
@@ -658,17 +669,18 @@ def wrap_text(text, width, max_lines=3):
 }
 
 # Load the existing dictionary of paper references
-with open("data/cleaned_complete_paper_references.json", "r") as file:
+with open("/Users/aayushgupta/Documents/GitHub/Prompt_Systematic_Review/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json", "r") as file:
     paper_references = json.load(file)
 
-# Query each title and get citation counts
+# Initialize a dictionary for citation counts
 citation_counts = {}
+
+# Iterate over the title_to_technique mapping
 for title, technique in title_to_technique.items():
-    paper_id = query_paper_id(title, api_key)
-    if paper_id and paper_id in paper_references:
-        citation_count = len(paper_references[paper_id])
-        citation_counts[technique] = citation_count
-        print({technique}, {citation_count})
+    paper_id = query_paper_id(title, api_key)  # Get the paper ID for each title
+    # Count how many times each paper_id appears in the reference lists of other papers
+    citation_count = sum(paper_id in refs for refs in paper_references.values())
+    citation_counts[technique] = citation_count
 
 # Sort the citation counts in descending order
 sorted_citations = sorted(citation_counts.items(), key=lambda x: x[1], reverse=True)
@@ -678,19 +690,23 @@ def wrap_text(text, width, max_lines=3):
 
 # Create a vertical bar chart
 plt.figure(figsize=(30, 12))
-plt.bar(
-    sorted_techniques, sorted_counts, color=(45 / 255, 137 / 255, 145 / 255, 1)
-)  # RGBA color
+plt.bar(sorted_techniques, sorted_counts, color=(45 / 255, 137 / 255, 145 / 255, 1))  # RGBA color
+
+plt.yscale('log')
 
 # Rotate the x-axis labels by 45 degrees for better readability
-plt.xticks(rotation=45, ha="right")  # ha='right' aligns the labels at the right angle
+plt.xticks(rotation=45, ha="right")
+
+# Remove the top and right borders
+ax = plt.gca()  # Get current axes
+ax.spines['top'].set_visible(False)
+ax.spines['right'].set_visible(False)
 
 # Add labels and title
 plt.ylabel("Number of References")
 plt.xlabel("Technique")
-plt.title("Citation Counts for Techniques Based on Papers")
+# plt.title("Internal Citation Counts for Techniques Based on Papers")
 
 plt.tight_layout()  # Adjusts layout to prevent clipping of labels
 plt.show()
-
 # %%

From ecc4b006682936d3dc6b5de94703861f10aa8d87 Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Mon, 19 Feb 2024 00:34:25 -0500
Subject: [PATCH 05/19] Added Comments

---
 .../find_internal_reference_count.py          | 21 ++++++++++---------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/src/prompt_systematic_review/experiments/find_internal_reference_count.py b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
index 5e365a9..3835712 100644
--- a/src/prompt_systematic_review/experiments/find_internal_reference_count.py
+++ b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
@@ -1,4 +1,4 @@
-# %%
+# %% RUN 1st
 import os
 import requests
 import time
@@ -12,7 +12,7 @@
 api_key = os.getenv("SEMANTIC_SCHOLAR_API_KEY")
 
 
-# %%
+# %% RUN 2nd
 def rate_limited_request(url, headers, max_retries=3, delay=0.1):
     retries = 0
     while retries < max_retries:
@@ -100,7 +100,7 @@ def get_paper_title(paper_id, api_key):
         return None
 
 
-# %%
+# %%Run 3rd
 import os
 import csv
 import json
@@ -172,7 +172,7 @@ def extract_arxiv_id(url):
     json.dump(paper_references, json_file, indent=4)
 print("Data saved to revised_paper_references.json")
 
-# %%
+# %%RUN 4th
 # Second main to add important papers not in our original dataset
 
 
@@ -260,7 +260,7 @@ def extract_arxiv_id(url):
 print("Data saved to unmatched_titles_additional.json")
 
 
-# %%
+# %%Run 5th
 # Merge the two files
 
 # Load the existing data from both JSON files
@@ -282,7 +282,7 @@ def extract_arxiv_id(url):
 print("Merged data saved to complete_paper_references.json")
 
 
-# %%
+# %%Run 6th
 # Only keep refernces which refer to papers in our combined dataset
 
 # Load the merged paper references
@@ -302,7 +302,7 @@ def extract_arxiv_id(url):
 print("Cleaned data saved to cleaned_merged_paper_references.json")
 
 
-# %%
+# %SKIP
 # Histogram of the top 30 most cited papers by internal citation count
 
 import json
@@ -356,7 +356,7 @@ def extract_arxiv_id(url):
 plt.show()
 
 
-# %%
+# %%SKIP
 # Generate a graph of all the papers with more than 10 internal references
 
 import networkx as nx
@@ -422,7 +422,8 @@ def extract_arxiv_id(url):
 plt.show()
 
 
-# %%
+# %%Run - Update File Path
+
 import networkx as nx
 import matplotlib.pyplot as plt
 import textwrap
@@ -595,7 +596,7 @@ def wrap_text(text, width, max_lines=3):
 # plt.title("Directed Graph of Paper's Internal References", fontsize=50)
 plt.show()
 
-# %%
+# %%Run - Update File Path
 # Generate a graph of all the papers with more than 10 internal references
 import json
 import matplotlib.pyplot as plt

From 93ae4f4abfc380f6bc07d99712906c849d9a9864 Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Tue, 20 Feb 2024 22:21:44 -0500
Subject: [PATCH 06/19] Commented out unnecessary code

---
 .../find_internal_reference_count.py          | 196 +++++++++---------
 1 file changed, 98 insertions(+), 98 deletions(-)

diff --git a/src/prompt_systematic_review/experiments/find_internal_reference_count.py b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
index 3835712..6d1b330 100644
--- a/src/prompt_systematic_review/experiments/find_internal_reference_count.py
+++ b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
@@ -305,121 +305,121 @@ def extract_arxiv_id(url):
 # %SKIP
 # Histogram of the top 30 most cited papers by internal citation count
 
-import json
-import matplotlib.pyplot as plt
-import textwrap
-import matplotlib
+# import json
+# import matplotlib.pyplot as plt
+# import textwrap
+# import matplotlib
 
-# Set font properties globally
-matplotlib.rcParams.update(
-    {
-        "font.family": "Arial",  # You can replace 'Arial' with 'Helvetica' or another modern font
-        "font.size": 8,  # You can adjust this value as needed
-    }
-)
+# # Set font properties globally
+# matplotlib.rcParams.update(
+#     {
+#         "font.family": "Arial",  # You can replace 'Arial' with 'Helvetica' or another modern font
+#         "font.size": 8,  # You can adjust this value as needed
+#     }
+# )
 
-# Load the data
-with open("data/cleaned_complete_paper_references.json", "r") as file:
-    data = json.load(file)
+# # Load the data
+# with open("data/cleaned_complete_paper_references.json", "r") as file:
+#     data = json.load(file)
 
-# Count the number of references for each key
-reference_counts = {key: len(references) for key, references in data.items()}
+# # Count the number of references for each key
+# reference_counts = {key: len(references) for key, references in data.items()}
 
-# Sort by the number of references and select the top 50
-top_25 = sorted(reference_counts.items(), key=lambda x: x[1], reverse=True)[:30]
+# # Sort by the number of references and select the top 50
+# top_25 = sorted(reference_counts.items(), key=lambda x: x[1], reverse=True)[:30]
 
-# Fetch the titles for the top 50 paper IDs
-top_25_titles = {paper_id: get_paper_title(paper_id, api_key) for paper_id, _ in top_25}
+# # Fetch the titles for the top 50 paper IDs
+# top_25_titles = {paper_id: get_paper_title(paper_id, api_key) for paper_id, _ in top_25}
 
-# Replace paper IDs with their titles in the top_50 list
-top_25_with_titles = [(top_25_titles[paper_id], count) for paper_id, count in top_25]
+# # Replace paper IDs with their titles in the top_50 list
+# top_25_with_titles = [(top_25_titles[paper_id], count) for paper_id, count in top_25]
 
-# Unpack the data for plotting
-titles, counts = zip(*top_25_with_titles)
+# # Unpack the data for plotting
+# titles, counts = zip(*top_25_with_titles)
 
-# Define the RGBA color
-rgba_color = (45 / 255, 137 / 255, 145 / 255, 1)  # Converted from rgba(45, 137, 145, 1)
+# # Define the RGBA color
+# rgba_color = (45 / 255, 137 / 255, 145 / 255, 1)  # Converted from rgba(45, 137, 145, 1)
 
-# Create a vertical bar chart
-plt.figure(figsize=(12, 8))
-plt.bar(titles, counts, color=rgba_color)
+# # Create a vertical bar chart
+# plt.figure(figsize=(12, 8))
+# plt.bar(titles, counts, color=rgba_color)
 
-# Rotate the x-axis labels by 45 degrees for better readability
-plt.xticks(rotation=45, ha="right")  # ha='right' aligns the labels at the right angle
+# # Rotate the x-axis labels by 45 degrees for better readability
+# plt.xticks(rotation=45, ha="right")  # ha='right' aligns the labels at the right angle
 
-# Add labels and title
-plt.ylabel("Number of References")
-plt.xlabel("Paper Title")
-plt.title("Top 30 Papers by Number of References")
+# # Add labels and title
+# plt.ylabel("Number of References")
+# plt.xlabel("Paper Title")
+# plt.title("Top 30 Papers by Number of References")
 
-# plt.tight_layout()  # Adjusts layout to prevent clipping of labels
-plt.show()
+# # plt.tight_layout()  # Adjusts layout to prevent clipping of labels
+# plt.show()
 
 
 # %%SKIP
 # Generate a graph of all the papers with more than 10 internal references
 
-import networkx as nx
-import matplotlib.pyplot as plt
-import textwrap
-
-# Load the cleaned references
-with open("data/cleaned_complete_paper_references.json", "r") as json_file:
-    paper_references = json.load(json_file)
-
-# Create the graph
-G = nx.DiGraph()
-for paper_id, references in paper_references.items():
-    for ref_id in references:
-        G.add_edge(paper_id, ref_id)
-
-# Remove isolated nodes and nodes with less than 10 incoming edges
-G.remove_nodes_from(list(nx.isolates(G)))
-nodes_to_remove = [node for node in G.nodes() if G.in_degree(node) < 10]
-G.remove_nodes_from(nodes_to_remove)
-
-# Find the top 20 nodes with the most incoming edges
-top_nodes = sorted(G.nodes(), key=lambda n: G.in_degree(n), reverse=True)[:20]
-
-titles_above_threshold = {}
-for paper_id in top_nodes:
-    full_title = get_paper_title(paper_id, api_key)
-    if full_title:
-        display_title = title_mappings.get(
-            full_title, full_title
-        )  # Use the full title if not found in the dictionary
-        if len(display_title) > 50:
-            display_title = textwrap.shorten(
-                display_title, width=50, placeholder="..."
-            )  # Shorten if longer than 60s
-        titles_above_threshold[paper_id] = display_title
-
-# Cap the maximum node size to prevent too large nodes
-# max_size = 100000  # Maximum size for a node
-node_sizes = [G.in_degree(node) * 2000 for node in G.nodes()]
-
-# Draw the graph with adjusted layout parameters
-plt.figure(figsize=(60, 35))  # Increased figure size for more space
-pos = nx.kamada_kawai_layout(G, dist=None, scale=1)  # Adjust 'scale' as needed
-nx.draw(
-    G,
-    pos,
-    with_labels=False,
-    node_size=node_sizes,
-    node_color="skyblue",
-    edge_color="gray",
-    width=0.5,
-)
-
-# Assign and label top nodes with titles
-for node, label in titles_above_threshold.items():
-    x, y = pos[node]
-    wrapped_label = label.split("\n")
-    for i, line in enumerate(wrapped_label):
-        plt.text(x, y, line, fontsize=18, ha="center", va="center")
-
-plt.title("Graph of Paper References")
-plt.show()
+# import networkx as nx
+# import matplotlib.pyplot as plt
+# import textwrap
+
+# # Load the cleaned references
+# with open("data/cleaned_complete_paper_references.json", "r") as json_file:
+#     paper_references = json.load(json_file)
+
+# # Create the graph
+# G = nx.DiGraph()
+# for paper_id, references in paper_references.items():
+#     for ref_id in references:
+#         G.add_edge(paper_id, ref_id)
+
+# # Remove isolated nodes and nodes with less than 10 incoming edges
+# G.remove_nodes_from(list(nx.isolates(G)))
+# nodes_to_remove = [node for node in G.nodes() if G.in_degree(node) < 10]
+# G.remove_nodes_from(nodes_to_remove)
+
+# # Find the top 20 nodes with the most incoming edges
+# top_nodes = sorted(G.nodes(), key=lambda n: G.in_degree(n), reverse=True)[:20]
+
+# titles_above_threshold = {}
+# for paper_id in top_nodes:
+#     full_title = get_paper_title(paper_id, api_key)
+#     if full_title:
+#         display_title = title_mappings.get(
+#             full_title, full_title
+#         )  # Use the full title if not found in the dictionary
+#         if len(display_title) > 50:
+#             display_title = textwrap.shorten(
+#                 display_title, width=50, placeholder="..."
+#             )  # Shorten if longer than 60s
+#         titles_above_threshold[paper_id] = display_title
+
+# # Cap the maximum node size to prevent too large nodes
+# # max_size = 100000  # Maximum size for a node
+# node_sizes = [G.in_degree(node) * 2000 for node in G.nodes()]
+
+# # Draw the graph with adjusted layout parameters
+# plt.figure(figsize=(60, 35))  # Increased figure size for more space
+# pos = nx.kamada_kawai_layout(G, dist=None, scale=1)  # Adjust 'scale' as needed
+# nx.draw(
+#     G,
+#     pos,
+#     with_labels=False,
+#     node_size=node_sizes,
+#     node_color="skyblue",
+#     edge_color="gray",
+#     width=0.5,
+# )
+
+# # Assign and label top nodes with titles
+# for node, label in titles_above_threshold.items():
+#     x, y = pos[node]
+#     wrapped_label = label.split("\n")
+#     for i, line in enumerate(wrapped_label):
+#         plt.text(x, y, line, fontsize=18, ha="center", va="center")
+
+# plt.title("Graph of Paper References")
+# plt.show()
 
 
 # %%Run - Update File Path

From 7a6b60a587b4a852d58a468cc6b8d47358741ce1 Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Tue, 20 Feb 2024 22:43:35 -0500
Subject: [PATCH 07/19] blacked

---
 .../find_internal_reference_count.py          | 37 +++++++++++++------
 1 file changed, 25 insertions(+), 12 deletions(-)

diff --git a/src/prompt_systematic_review/experiments/find_internal_reference_count.py b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
index 6d1b330..e604ca9 100644
--- a/src/prompt_systematic_review/experiments/find_internal_reference_count.py
+++ b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
@@ -476,7 +476,10 @@ def adjust_overlap(
 
 
 # Load the cleaned references
-with open("/Users/aayushgupta/Documents/GitHub/Prompt_Systematic_Review/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json", "r") as json_file:
+with open(
+    "/Users/aayushgupta/Documents/GitHub/Prompt_Systematic_Review/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json",
+    "r",
+) as json_file:
     paper_references = json.load(json_file)
 
 # Create the graph
@@ -514,8 +517,6 @@ def adjust_overlap(
 }
 
 
-
-
 # Find the top 20 nodes with the most incoming edges
 top_nodes = sorted(G.nodes(), key=lambda n: G.in_degree(n), reverse=True)[:25]
 
@@ -523,9 +524,15 @@ def adjust_overlap(
 print("Top 20 Referenced Papers:")
 for paper_id in top_nodes:
     in_degree = G.in_degree(paper_id)  # The number of references
-    full_title = get_paper_title(paper_id, api_key)  # Assuming this function fetches the paper title
-    display_title = title_to_technique.get(full_title, full_title)  # Use mapped value if exists, otherwise full title
-    print(f'"{display_title}" referenced by {in_degree} other papers and its paper_id was {paper_id}')
+    full_title = get_paper_title(
+        paper_id, api_key
+    )  # Assuming this function fetches the paper title
+    display_title = title_to_technique.get(
+        full_title, full_title
+    )  # Use mapped value if exists, otherwise full title
+    print(
+        f'"{display_title}" referenced by {in_degree} other papers and its paper_id was {paper_id}'
+    )
 
 
 # Remove isolated nodes and nodes with less than 10 incoming edges
@@ -533,6 +540,7 @@ def adjust_overlap(
 nodes_to_remove = [node for node in G.nodes() if G.in_degree(node) < 8]
 G.remove_nodes_from(nodes_to_remove)
 
+
 # Define a function to wrap text into at most two lines
 def wrap_text(text, width, max_lines=3):
     wrapped_lines = textwrap.wrap(text, width)
@@ -591,7 +599,7 @@ def wrap_text(text, width, max_lines=3):
     plt.text(
         x, y + y_offset, label, fontsize=font_sizes[node], ha="center", va="center"
     )
-plt.axis('off')
+plt.axis("off")
 
 # plt.title("Directed Graph of Paper's Internal References", fontsize=50)
 plt.show()
@@ -670,7 +678,10 @@ def wrap_text(text, width, max_lines=3):
 }
 
 # Load the existing dictionary of paper references
-with open("/Users/aayushgupta/Documents/GitHub/Prompt_Systematic_Review/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json", "r") as file:
+with open(
+    "/Users/aayushgupta/Documents/GitHub/Prompt_Systematic_Review/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json",
+    "r",
+) as file:
     paper_references = json.load(file)
 
 # Initialize a dictionary for citation counts
@@ -691,17 +702,19 @@ def wrap_text(text, width, max_lines=3):
 
 # Create a vertical bar chart
 plt.figure(figsize=(30, 12))
-plt.bar(sorted_techniques, sorted_counts, color=(45 / 255, 137 / 255, 145 / 255, 1))  # RGBA color
+plt.bar(
+    sorted_techniques, sorted_counts, color=(45 / 255, 137 / 255, 145 / 255, 1)
+)  # RGBA color
 
-plt.yscale('log')
+plt.yscale("log")
 
 # Rotate the x-axis labels by 45 degrees for better readability
 plt.xticks(rotation=45, ha="right")
 
 # Remove the top and right borders
 ax = plt.gca()  # Get current axes
-ax.spines['top'].set_visible(False)
-ax.spines['right'].set_visible(False)
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
 
 # Add labels and title
 plt.ylabel("Number of References")

From c75e94627b922936b84e10cd005ff6297f56cb55 Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Mon, 11 Mar 2024 01:56:43 -0400
Subject: [PATCH 08/19] Updated graph with nodes and edges

---
 .../find_internal_reference_count.py          | 249 ++++++++++--------
 1 file changed, 134 insertions(+), 115 deletions(-)

diff --git a/src/prompt_systematic_review/experiments/find_internal_reference_count.py b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
index e604ca9..3c3cf8f 100644
--- a/src/prompt_systematic_review/experiments/find_internal_reference_count.py
+++ b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
@@ -53,6 +53,50 @@ def get_references(paper_id, api_key):
         return reference_ids
     return []
 
+def get_citation_counts(paper_ids):
+    """
+    Fetches citation counts for a list of paper IDs from Semantic Scholar's API.
+
+    Parameters:
+    - paper_ids (list): A list of paper IDs (strings).
+
+    Returns:
+    - dict: A dictionary where keys are paper IDs and values are citation counts.
+    """
+    # Define the URL for the batch API endpoint
+    url = 'https://api.semanticscholar.org/graph/v1/paper/batch'
+    
+    # Define the parameters for the fields you want to retrieve
+    params = {'fields': 'citationCount'}
+    
+    # The payload for the POST request, including the list of paper IDs
+    payload = {"ids": paper_ids}
+    
+    # Make the POST request to the batch API endpoint
+    response = requests.post(url, params=params, json=payload)
+    
+    # Initialize an empty dictionary to store the citation counts
+    citation_counts = {}
+    
+    # Check if the request was successful
+    if response.status_code == 200:
+        # Parse the JSON response
+        data = response.json()
+        
+        # Loop through the papers in the response
+        for paper in data:
+            # Extract the paper ID and citation count
+            paper_id = paper.get('paperId')
+            citation_count = paper.get('citationCount', 0)
+            
+            # Add the citation count to the dictionary
+            citation_counts[paper_id] = citation_count
+    else:
+        print(f"Failed to fetch data: {response.status_code}")
+    
+    return citation_counts
+
+
 
 # Function to query paper title from Arxiv Id
 def get_arxiv_paper_title(arxiv_id):
@@ -296,7 +340,7 @@ def extract_arxiv_id(url):
     ]
 
 # Save the cleaned data back to a JSON file
-with open("data/cleaned_complete_paper_references.json", "w") as file:
+with open("cleaned_complete_paper_references.json", "w") as file:
     json.dump(merged_paper_references, file, indent=4)
 
 print("Cleaned data saved to cleaned_merged_paper_references.json")
@@ -429,50 +473,40 @@ def extract_arxiv_id(url):
 import textwrap
 
 
-def adjust_overlap(
-    pos, nodes_to_adjust, min_dist=1.0, repulsion_factor=1.05, vertical_bias=2.0
-):
-    for _ in range(1000):  # Increase the number of iterations for a denser graph
+# Function to adjust node positions to maintain a min and max distance
+def adjust_overlap(pos, nodes_to_adjust, min_dist=1.0, max_dist=2.0, repulsion_factor=1.05, attraction_factor=0.95, vertical_bias=2.0):
+    for _ in range(1000):  # Iteration limit
         adjusted = False
         for node1 in nodes_to_adjust:
             for node2 in nodes_to_adjust:
                 if node1 == node2:
-                    continue  # Skip comparing the node to itself
+                    continue  # Skip self comparisons
                 x1, y1 = pos[node1]
                 x2, y2 = pos[node2]
                 dx, dy = x1 - x2, y1 - y2
-                dist = (dx**2 + dy**2) ** 0.5
-                if dist < min_dist:  # If nodes are too close, push them apart
-                    if dist == 0:  # To avoid division by zero
-                        dx, dy = 1, 0
-                        dist = 1
-                    dx, dy = (
-                        dx / dist * min_dist * repulsion_factor,
-                        dy / dist * min_dist * repulsion_factor,
-                    )
-
-                    # Apply additional vertical adjustment
-                    dy *= vertical_bias
-
-                    # Update the position of node1
-                    new_x1, new_y1 = x1 + dx, y1 + dy
-
-                    # Check if the new position of node1 overlaps with other nodes
-                    overlap = False
-                    for other_node in nodes_to_adjust:
-                        if other_node == node1 or other_node == node2:
-                            continue
-                        ox, oy = pos[other_node]
-                        if ((new_x1 - ox) ** 2 + (new_y1 - oy) ** 2) ** 0.5 < min_dist:
-                            overlap = True
-                            break
-
-                    if not overlap:
-                        pos[node1] = new_x1, new_y1
-                        adjusted = True
-
-        if not adjusted:  # Break the loop if no adjustments were made
-            break
+                dist = (dx**2 + dy**2) ** 0.5  # Euclidean distance
+                
+                if dist < min_dist:  # Nodes too close, push apart
+                    factor = repulsion_factor
+                elif dist > max_dist:  # Nodes too far, pull together
+                    factor = -attraction_factor
+                else:
+                    continue  # Distance is acceptable, no adjustment
+                
+                if dist == 0:  # Prevent division by zero
+                    dx, dy = 1, 0
+                    dist = 1
+                
+                dx, dy = dx / dist * min_dist * factor, dy / dist * min_dist * factor
+                dy *= vertical_bias  # Apply vertical adjustment
+                
+                # Adjust positions
+                pos[node1] = (x1 + dx, y1 + dy)
+                pos[node2] = (x2 - dx, y2 - dy)
+                adjusted = True
+
+        if not adjusted:
+            break  # Stop if no adjustments were made in a full pass
 
 
 # Load the cleaned references
@@ -488,58 +522,52 @@ def adjust_overlap(
     for ref_id in references:
         G.add_edge(paper_id, ref_id)
 
-title_to_technique = {
-    "Language Models are Few-Shot Learners": "Few-Shot Prompting",
-    "Calibrate Before Use: Improving Few-Shot Performance of Language Models": "Calibration for FSL",
-    "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity": "Example Ordering",
-    "What Makes Good In-Context Examples for GPT-3?": "In-Context Learning (ICL)",
-    "Making Pre-trained Language Models Better Few-shot Learners": "Pre-trained FSL Improvement",
-    "Self-Consistency Improves Chain of Thought Reasoning in Language Models": "Self-Consistency",
-    "Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?": "Example Label Quality",
-    "Large Language Models are Zero-Shot Reasoners": "Zero-Shot-CoT",
-    "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models": "Least-to-Most Prompting",
-    "True Few-Shot Learning with Language Models": "True FSL",
-    "Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm": "Beyond the Few-Shot Paradigm",
-    "Learning To Retrieve Prompts for In-Context Learning": "Prompt Retrieval Learning",
-    "An Explanation of In-context Learning as Implicit Bayesian Inference": "Implicit Bayesian Inference",
-    "OPT: Open Pre-trained Transformer Language Models": "Open Pre-trained Transformer (OPT)",
-    "Do Prompt-Based Models Really Understand the Meaning of Their Prompts?": "Prompt Meaning Understanding",
-    "MetaICL: Learning to Learn In Context": "Meta In-Context Learning (MetaICL)",
-    "Noisy Channel Language Model Prompting for Few-Shot Text Classification": "Noisy Channel Prompting",
-    "Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them": "BIG-Bench & CoT",
-    "Can language models learn from explanations in context?": "Learning from Explanations",
-    "Reframing Instructional Prompts to GPTk’s Language": "Instructional Prompt Reframing",
-    "Meta-learning via Language Model In-context Tuning": "Meta-Learning LM Tuning",
-    "Program Synthesis with Large Language Models": "Program Synthesis LLM",
-    "Selective Annotation Makes Language Models Better Few-Shot Learners": "Input Distribution",
+
+technique_to_title = {
+    "Language Models are Few-Shot Learners": "Few-Shot Learning",
+    "A Survey on In-context Learning": "In-context Learning Survey",
+    "Exploring Demonstration Ensembling for In-context Learning": "Demonstration Ensembling",
+    "Unified Demonstration Retriever for In-Context Learning": "Unified Demo Retriever",
+    "Finding Support Examples for In-Context Learning": "Support Examples",
+    "Large Language Models Are Human-Level Prompt Engineers": "Human-Level Prompting",
     "Measuring and Narrowing the Compositionality Gap in Language Models": "Compositionality Gap",
-    "Rationale-Augmented Ensembles in Language Models": "Rationale-Augmented Ensembles",
+    "Automatic Chain of Thought Prompting in Large Language Models": "Automatic CoT",
+    "Complexity-Based Prompting for Multi-Step Reasoning": "Complexity-Based Prompting",
+    "Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator": "Self-Generated ICL",
+    "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models": "Least-to-Most Prompting",
+    "Learning To Retrieve Prompts for In-Context Learning": "Prompt Retrieval",
+    "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity": "Prompt Order Sensitivity",
+    "What Makes Good In-Context Examples for GPT-3?": "Good In-Context Examples",
+    "MoT: Memory-of-Thought Enables ChatGPT to Self-Improve": "Memory-of-Thought",
+    "kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference": "kNN Prompting",
+    "Large Language Models are Zero-Shot Reasoners": "Zero-Shot Reasoning",
+    "Self-Consistency Improves Chain of Thought Reasoning in Language Models": "Self-Consistency",
+    "Large Language Models as Optimizers": "LLMs as Optimizers",
+    "Decomposed Prompting: A Modular Approach for Solving Complex Tasks": "Decomposed Prompting",
+    "Is a Question Decomposition Unit All We Need?": "Question Decomposition",
+    "Deductive Verification of Chain-of-Thought Reasoning": "Deductive Verification",
+    "Active Prompting with Chain-of-Thought for Large Language Models": "Active Prompting",
+    "Large Language Model Guided Tree-of-Thought": "LLM Guided ToT",
+    "Language Models (Mostly) Know What They Know": "LLM Self-Knowledge",
+    "Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data": "Automatic Prompt Augmentation",
+    "Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations": "Maieutic Prompting",
+    "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models": "Plan-and-Solve Prompting",
+    "Tree of Thoughts: Deliberate Problem Solving with Large Language Models": "Tree of Thoughts",
+    "Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks": "Program of Thoughts",
+    "Self-Refine: Iterative Refinement with Self-Feedback": "Self-Refine",
+    "Cumulative Reasoning with Large Language Models": "Cumulative Reasoning",
+    "Faithful Chain-of-Thought Reasoning": "Faithful CoT",
+    "Making Language Models Better Reasoners with Step-Aware Verifier": "Step-Aware Verification",
+    "Graph of Thoughts: Solving Elaborate Problems with Large Language Models": "Graph of Thoughts",
+    "Chain-of-Verification Reduces Hallucination in Large Language Models": "Chain-of-Verification",
+    "Better Zero-Shot Reasoning with Self-Adaptive Prompting": "Self-Adaptive Prompting",
+    "Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves": "Rephrase and Respond"
 }
+# Find the nodes with at least one incoming edge
+nodes_with_incoming_edges = [node for node in G.nodes() if G.in_degree(node) > 0]
 
-
-# Find the top 20 nodes with the most incoming edges
 top_nodes = sorted(G.nodes(), key=lambda n: G.in_degree(n), reverse=True)[:25]
 
-# Print the name of the paper and the amount of references for the top 20 nodes
-print("Top 20 Referenced Papers:")
-for paper_id in top_nodes:
-    in_degree = G.in_degree(paper_id)  # The number of references
-    full_title = get_paper_title(
-        paper_id, api_key
-    )  # Assuming this function fetches the paper title
-    display_title = title_to_technique.get(
-        full_title, full_title
-    )  # Use mapped value if exists, otherwise full title
-    print(
-        f'"{display_title}" referenced by {in_degree} other papers and its paper_id was {paper_id}'
-    )
-
-
-# Remove isolated nodes and nodes with less than 10 incoming edges
-G.remove_nodes_from(list(nx.isolates(G)))
-nodes_to_remove = [node for node in G.nodes() if G.in_degree(node) < 8]
-G.remove_nodes_from(nodes_to_remove)
-
 
 # Define a function to wrap text into at most two lines
 def wrap_text(text, width, max_lines=3):
@@ -552,57 +580,48 @@ def wrap_text(text, width, max_lines=3):
     return "\n".join(wrapped_lines)
 
 
-# Adjusted part of your code for assigning and labeling top nodes with titles
+# Assign and label nodes with titles
 titles_above_threshold = {}
 for paper_id in top_nodes:
     full_title = get_paper_title(paper_id, api_key)  # Fetch full paper title
     if full_title:
-        # Check if the full title is in your dictionary and use the mapped value if it is
-        display_title = title_to_technique.get(
-            full_title, full_title
-        )  # Use the full title if not found in the dictionary
-        wrapped_title = wrap_text(
-            display_title, 16
-        )  # Wrap the title (or its mapped value)
+        display_title = technique_to_title.get(full_title, full_title)
+        wrapped_title = wrap_text(display_title, 10)
         titles_above_threshold[paper_id] = wrapped_title
 
-
-# Cap the maximum node size to prevent too large nodes
-node_sizes = [G.in_degree(node) * 2000 for node in G.nodes()]
+# Set node sizes proportional to the number of incoming edges (increased size)
+node_sizes = [((G.in_degree(node) * 3000)+1000) for node in top_nodes]
 
 # Calculate font size based on in-degree (you can adjust the scaling factor)
-font_sizes = {
-    node: G.in_degree(node) * 0.2 + 14 for node in G.nodes()
-}  # '+ 10' ensures a minimum font size
+font_sizes = {node: G.in_degree(node) * 0.60 + 14 for node in top_nodes}
 
 # Draw the graph with adjusted layout parameters
-plt.figure(figsize=(60, 35))
-pos = nx.kamada_kawai_layout(G, dist=None, scale=1)
+plt.figure(figsize=(50, 30))
 
-adjust_overlap(pos, top_nodes, min_dist=0.2, repulsion_factor=1.05)
+pos = nx.spring_layout(G, k=.3, iterations=50, scale=2)  # Initial layout
+adjust_overlap(pos, top_nodes, min_dist=1, max_dist=7.5)  # Adjust node positions
 
-# Draw all nodes first
+# Draw nodes with incoming edges
 nx.draw_networkx_nodes(
-    G, pos, node_size=node_sizes, node_color=(45 / 255, 137 / 255, 145 / 255, 1)
+    G, pos, nodelist=top_nodes, node_size=node_sizes, node_color=(45/255, 137/255, 145/255, 1)
 )
 
 # Draw the edges
-nx.draw_networkx_edges(G, pos, edge_color="gray", width=0.5)
+nx.draw_networkx_edges(G, pos, edge_color="gray", width=0.3)
 
-# Assign and label top nodes with titles
+# Assign and label nodes with titles without y_offset
 for node, label in titles_above_threshold.items():
     x, y = pos[node]
-    num_lines = label.count("\n") + 1
-    y_offset = (
-        0.005 * num_lines
-    )  # Adjust this factor as needed to position the text correctly
-    plt.text(
-        x, y + y_offset, label, fontsize=font_sizes[node], ha="center", va="center"
-    )
-plt.axis("off")
+    plt.text(x, y, label, fontsize=font_sizes[node], ha="center", va="center", wrap=True)
 
-# plt.title("Directed Graph of Paper's Internal References", fontsize=50)
+plt.axis("off")
 plt.show()
+# # Print the names of all the nodes
+# print("Names of all nodes:")
+# for node in top_nodes:
+#     full_title = get_paper_title(node, api_key)
+#     if full_title:
+#         print(full_title)
 
 # %%Run - Update File Path
 # Generate a graph of all the papers with more than 10 internal references

From 66a52c20bea0d31a3dc30720c8facb1ca8d4a047 Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Tue, 16 Apr 2024 22:18:49 -0400
Subject: [PATCH 09/19] Cleaned code

---
 .../find_internal_reference_count.py          | 147 ++----------------
 1 file changed, 12 insertions(+), 135 deletions(-)

diff --git a/src/prompt_systematic_review/experiments/find_internal_reference_count.py b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
index 3c3cf8f..bebfe5c 100644
--- a/src/prompt_systematic_review/experiments/find_internal_reference_count.py
+++ b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
@@ -1,3 +1,8 @@
+#%% Pip Installs
+%pip install tqdm
+%pip install pandas
+%pip install scipy
+
 # %% RUN 1st
 import os
 import requests
@@ -10,8 +15,6 @@
 
 load_dotenv()
 api_key = os.getenv("SEMANTIC_SCHOLAR_API_KEY")
-
-
 # %% RUN 2nd
 def rate_limited_request(url, headers, max_retries=3, delay=0.1):
     retries = 0
@@ -151,9 +154,8 @@ def get_paper_title(paper_id, api_key):
 from tqdm import tqdm
 import random
 
-
 # Path to the CSV file containing the papers' data
-csv_file_path = "./data/master_papers.csv"
+csv_file_path = "../../../data/master_papers.csv"
 
 # Dictionary to hold the references
 paper_references = {}
@@ -219,7 +221,6 @@ def extract_arxiv_id(url):
 # %%RUN 4th
 # Second main to add important papers not in our original dataset
 
-
 paper_references = {}
 unmatched_titles = {}
 
@@ -346,128 +347,8 @@ def extract_arxiv_id(url):
 print("Cleaned data saved to cleaned_merged_paper_references.json")
 
 
-# %SKIP
-# Histogram of the top 30 most cited papers by internal citation count
-
-# import json
-# import matplotlib.pyplot as plt
-# import textwrap
-# import matplotlib
-
-# # Set font properties globally
-# matplotlib.rcParams.update(
-#     {
-#         "font.family": "Arial",  # You can replace 'Arial' with 'Helvetica' or another modern font
-#         "font.size": 8,  # You can adjust this value as needed
-#     }
-# )
-
-# # Load the data
-# with open("data/cleaned_complete_paper_references.json", "r") as file:
-#     data = json.load(file)
-
-# # Count the number of references for each key
-# reference_counts = {key: len(references) for key, references in data.items()}
-
-# # Sort by the number of references and select the top 50
-# top_25 = sorted(reference_counts.items(), key=lambda x: x[1], reverse=True)[:30]
-
-# # Fetch the titles for the top 50 paper IDs
-# top_25_titles = {paper_id: get_paper_title(paper_id, api_key) for paper_id, _ in top_25}
-
-# # Replace paper IDs with their titles in the top_50 list
-# top_25_with_titles = [(top_25_titles[paper_id], count) for paper_id, count in top_25]
-
-# # Unpack the data for plotting
-# titles, counts = zip(*top_25_with_titles)
-
-# # Define the RGBA color
-# rgba_color = (45 / 255, 137 / 255, 145 / 255, 1)  # Converted from rgba(45, 137, 145, 1)
-
-# # Create a vertical bar chart
-# plt.figure(figsize=(12, 8))
-# plt.bar(titles, counts, color=rgba_color)
-
-# # Rotate the x-axis labels by 45 degrees for better readability
-# plt.xticks(rotation=45, ha="right")  # ha='right' aligns the labels at the right angle
-
-# # Add labels and title
-# plt.ylabel("Number of References")
-# plt.xlabel("Paper Title")
-# plt.title("Top 30 Papers by Number of References")
-
-# # plt.tight_layout()  # Adjusts layout to prevent clipping of labels
-# plt.show()
-
-
-# %%SKIP
-# Generate a graph of all the papers with more than 10 internal references
-
-# import networkx as nx
-# import matplotlib.pyplot as plt
-# import textwrap
-
-# # Load the cleaned references
-# with open("data/cleaned_complete_paper_references.json", "r") as json_file:
-#     paper_references = json.load(json_file)
-
-# # Create the graph
-# G = nx.DiGraph()
-# for paper_id, references in paper_references.items():
-#     for ref_id in references:
-#         G.add_edge(paper_id, ref_id)
-
-# # Remove isolated nodes and nodes with less than 10 incoming edges
-# G.remove_nodes_from(list(nx.isolates(G)))
-# nodes_to_remove = [node for node in G.nodes() if G.in_degree(node) < 10]
-# G.remove_nodes_from(nodes_to_remove)
-
-# # Find the top 20 nodes with the most incoming edges
-# top_nodes = sorted(G.nodes(), key=lambda n: G.in_degree(n), reverse=True)[:20]
-
-# titles_above_threshold = {}
-# for paper_id in top_nodes:
-#     full_title = get_paper_title(paper_id, api_key)
-#     if full_title:
-#         display_title = title_mappings.get(
-#             full_title, full_title
-#         )  # Use the full title if not found in the dictionary
-#         if len(display_title) > 50:
-#             display_title = textwrap.shorten(
-#                 display_title, width=50, placeholder="..."
-#             )  # Shorten if longer than 60s
-#         titles_above_threshold[paper_id] = display_title
-
-# # Cap the maximum node size to prevent too large nodes
-# # max_size = 100000  # Maximum size for a node
-# node_sizes = [G.in_degree(node) * 2000 for node in G.nodes()]
-
-# # Draw the graph with adjusted layout parameters
-# plt.figure(figsize=(60, 35))  # Increased figure size for more space
-# pos = nx.kamada_kawai_layout(G, dist=None, scale=1)  # Adjust 'scale' as needed
-# nx.draw(
-#     G,
-#     pos,
-#     with_labels=False,
-#     node_size=node_sizes,
-#     node_color="skyblue",
-#     edge_color="gray",
-#     width=0.5,
-# )
-
-# # Assign and label top nodes with titles
-# for node, label in titles_above_threshold.items():
-#     x, y = pos[node]
-#     wrapped_label = label.split("\n")
-#     for i, line in enumerate(wrapped_label):
-#         plt.text(x, y, line, fontsize=18, ha="center", va="center")
-
-# plt.title("Graph of Paper References")
-# plt.show()
-
-
 # %%Run - Update File Path
-
+import scipy 
 import networkx as nx
 import matplotlib.pyplot as plt
 import textwrap
@@ -510,9 +391,7 @@ def adjust_overlap(pos, nodes_to_adjust, min_dist=1.0, max_dist=2.0, repulsion_f
 
 
 # Load the cleaned references
-with open(
-    "/Users/aayushgupta/Documents/GitHub/Prompt_Systematic_Review/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json",
-    "r",
+with open("cleaned_complete_paper_references.json","r",
 ) as json_file:
     paper_references = json.load(json_file)
 
@@ -590,10 +469,10 @@ def wrap_text(text, width, max_lines=3):
         titles_above_threshold[paper_id] = wrapped_title
 
 # Set node sizes proportional to the number of incoming edges (increased size)
-node_sizes = [((G.in_degree(node) * 3000)+1000) for node in top_nodes]
+node_sizes = [((G.in_degree(node) * 300)+1000) for node in top_nodes]
 
 # Calculate font size based on in-degree (you can adjust the scaling factor)
-font_sizes = {node: G.in_degree(node) * 0.60 + 14 for node in top_nodes}
+font_sizes = {node: G.in_degree(node) * 0.06 + 14 for node in top_nodes}
 
 # Draw the graph with adjusted layout parameters
 plt.figure(figsize=(50, 30))
@@ -623,7 +502,7 @@ def wrap_text(text, width, max_lines=3):
 #     if full_title:
 #         print(full_title)
 
-# %%Run - Update File Path
+# %%Run 
 # Generate a graph of all the papers with more than 10 internal references
 import json
 import matplotlib.pyplot as plt
@@ -698,9 +577,7 @@ def wrap_text(text, width, max_lines=3):
 
 # Load the existing dictionary of paper references
 with open(
-    "/Users/aayushgupta/Documents/GitHub/Prompt_Systematic_Review/src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json",
-    "r",
-) as file:
+    "cleaned_complete_paper_references.json","r",) as file:
     paper_references = json.load(file)
 
 # Initialize a dictionary for citation counts

From 21dd7996d7bf87202f1454c3612fbe47a813456a Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Wed, 24 Apr 2024 21:28:31 -0400
Subject: [PATCH 10/19] Created as a python file

---
 .../experiments/graph_internal_references.py  | 444 ++++++++++++++++++
 1 file changed, 444 insertions(+)
 create mode 100644 src/prompt_systematic_review/experiments/graph_internal_references.py

diff --git a/src/prompt_systematic_review/experiments/graph_internal_references.py b/src/prompt_systematic_review/experiments/graph_internal_references.py
new file mode 100644
index 0000000..825e78a
--- /dev/null
+++ b/src/prompt_systematic_review/experiments/graph_internal_references.py
@@ -0,0 +1,444 @@
+import os
+import requests
+import time
+import json
+from tqdm import tqdm
+import pandas as pd
+from dotenv import load_dotenv
+import csv
+import random
+import scipy
+import networkx as nx
+import matplotlib.pyplot as plt
+import textwrap
+import matplotlib
+
+class SemanticScholarAPI:
+    def __init__(self, api_key):
+        self.api_key = api_key
+
+    def rate_limited_request(self, url, headers, max_retries=3, delay=0.1):
+        retries = 0
+        while retries < max_retries:
+            try:
+                time.sleep(delay)
+                response = requests.get(url, headers=headers)
+                response.raise_for_status()
+                return response
+            except requests.exceptions.HTTPError as e:
+                if response.status_code == 429 or response.status_code == 504:
+                    print(f"Received status code {response.status_code}. Retrying...")
+                    delay *= 2  # Exponential backoff
+                    retries += 1
+                else:
+                    print(f"HTTPError: {e}")
+                    break  # For other HTTP errors, don't retry
+            except requests.RequestException as e:
+                print(f"Request error: {e}")
+                break  # For non-HTTP errors, don't retry
+        return None
+
+    def get_references(self, paper_id):
+        url = f"https://api.semanticscholar.org/graph/v1/paper/{paper_id}/references?fields=title,authors&limit=1000"
+        headers = {"x-api-key": self.api_key}
+        response = self.rate_limited_request(url, headers)
+
+        if response and response.status_code == 200:
+            data = response.json()
+            reference_ids = []
+
+            if "data" in data:
+                for ref in data["data"]:
+                    if "citedPaper" in ref and "paperId" in ref["citedPaper"]:
+                        reference_ids.append(ref["citedPaper"]["paperId"])
+
+            return reference_ids
+        return []
+
+    def get_citation_counts(self, paper_ids):
+        url = 'https://api.semanticscholar.org/graph/v1/paper/batch'
+        params = {'fields': 'citationCount'}
+        payload = {"ids": paper_ids}
+        response = requests.post(url, params=params, json=payload)
+        citation_counts = {}
+
+        if response.status_code == 200:
+            data = response.json()
+            for paper in data:
+                paper_id = paper.get('paperId')
+                citation_count = paper.get('citationCount', 0)
+                citation_counts[paper_id] = citation_count
+        else:
+            print(f"Failed to fetch data: {response.status_code}")
+
+        return citation_counts
+
+    def query_paper_id(self, title):
+        url = f"https://api.semanticscholar.org/graph/v1/paper/search?query={title}&limit=1"
+        headers = {"x-api-key": self.api_key}
+        response = self.rate_limited_request(url, headers, delay=1, max_retries=4)
+        if response.status_code == 200:
+            data = response.json()
+            if "data" in data and data["data"]:
+                return data["data"][0]["paperId"]
+        return None
+
+    def get_paper_title(self, paper_id):
+        url = f"https://api.semanticscholar.org/graph/v1/paper/{paper_id}"
+        headers = {"x-api-key": self.api_key}
+        response = self.rate_limited_request(url, headers)
+
+        if response and response.status_code == 200:
+            data = response.json()
+            return data.get("title")
+        else:
+            print(f"Failed to fetch data for paper ID: {paper_id}")
+            return None
+
+
+class ArxivAPI:
+    def get_arxiv_paper_title(self, arxiv_id):
+        arxiv_url = f"http://export.arxiv.org/api/query?id_list={arxiv_id}"
+        response = requests.get(arxiv_url)
+        if response.status_code != 200:
+            return None
+        start = response.text.find("<title>") + 7
+        end = response.text.find("</title>", start)
+        title = response.text[start:end].strip()
+        return title
+
+
+class PaperProcessor:
+    def __init__(self, api_key):
+        self.semantic_scholar_api = SemanticScholarAPI(api_key)
+        self.arxiv_api = ArxivAPI()
+
+    def extract_arxiv_id(self, url):
+        base = url.rsplit("/", 1)[-1]
+        arxiv_id_with_version = base.split(".pdf")[0]
+        arxiv_id = (
+            arxiv_id_with_version.split("v")[0]
+            if "v" in arxiv_id_with_version
+            else arxiv_id_with_version
+        )
+        return arxiv_id
+
+    def convert_arxiv_id_to_semantic_scholar_id(self, arxiv_id):
+        title = self.arxiv_api.get_arxiv_paper_title(arxiv_id)
+        if title:
+            return self.semantic_scholar_api.query_paper_id(title)
+        return None
+
+    def process_papers(self, csv_file_path):
+        paper_references = {}
+        unmatched_papers = {}
+
+        if os.path.exists(csv_file_path):
+            with open(csv_file_path, mode="r", encoding="utf-8") as csvfile:
+                csv_reader = csv.DictReader(csvfile, delimiter=",")
+                all_papers = list(csv_reader)
+                for row in tqdm(all_papers, desc="Processing Papers"):
+                    source = row.get("source", "").strip()
+                    if source == "Semantic Scholar":
+                        paper_id = row.get("paperId", "").strip()
+                    elif source == "arXiv":
+                        arxiv_paper_id = self.extract_arxiv_id(row.get("url", "").strip())
+                        paper_id = self.convert_arxiv_id_to_semantic_scholar_id(arxiv_paper_id)
+                    else:
+                        unmatched_papers[row.get("title", "").strip()] = "Source not supported"
+                        continue
+
+                    if paper_id:
+                        references = self.semantic_scholar_api.get_references(paper_id)
+                        if references is not None:
+                            paper_references[paper_id] = references
+                        else:
+                            unmatched_papers[row["title"]] = "No references found or error occurred"
+                    else:
+                        print(f"Paper Id Could not be found for: {row}")
+        else:
+            print(f"CSV file does not exist: {csv_file_path}")
+
+        return paper_references, unmatched_papers
+
+    def process_additional_papers(self, titles):
+        paper_references = {}
+        unmatched_titles = {}
+
+        for title in tqdm(titles):
+            paper_id = self.semantic_scholar_api.query_paper_id(title)
+            references = self.semantic_scholar_api.get_references(paper_id)
+
+            if paper_id:
+                paper_references[paper_id] = references
+            else:
+                unmatched_titles[title] = "No matching paper ID found"
+
+        return paper_references, unmatched_titles
+
+
+class GraphVisualizer:
+    def __init__(self, api_key):
+        self.semantic_scholar_api = SemanticScholarAPI(api_key)
+
+    def adjust_overlap(self, pos, nodes_to_adjust, min_dist=1.0, max_dist=2.0, repulsion_factor=1.05, attraction_factor=0.95, vertical_bias=2.0):
+        for _ in range(1000):
+            adjusted = False
+            for node1 in nodes_to_adjust:
+                for node2 in nodes_to_adjust:
+                    if node1 == node2:
+                        continue
+                    x1, y1 = pos[node1]
+                    x2, y2 = pos[node2]
+                    dx, dy = x1 - x2, y1 - y2
+                    dist = (dx**2 + dy**2) ** 0.5
+
+                    if dist < min_dist:
+                        factor = repulsion_factor
+                    elif dist > max_dist:
+                        factor = -attraction_factor
+                    else:
+                        continue
+
+                    if dist == 0:
+                        dx, dy = 1, 0
+                        dist = 1
+
+                    dx, dy = dx / dist * min_dist * factor, dy / dist * min_dist * factor
+                    dy *= vertical_bias
+
+                    pos[node1] = (x1 + dx, y1 + dy)
+                    pos[node2] = (x2 - dx, y2 - dy)
+                    adjusted = True
+
+            if not adjusted:
+                break
+
+    def wrap_text(self, text, width, max_lines=3):
+        wrapped_lines = textwrap.wrap(text, width)
+        if len(wrapped_lines) > max_lines:
+            wrapped_lines = wrapped_lines[:max_lines]
+            wrapped_lines[-1] += "..."
+        return "\n".join(wrapped_lines)
+
+    def visualize_graph(self, paper_references, technique_to_title):
+        G = nx.DiGraph()
+        for paper_id, references in paper_references.items():
+            for ref_id in references:
+                G.add_edge(paper_id, ref_id)
+
+        nodes_with_incoming_edges = [node for node in G.nodes() if G.in_degree(node) > 0]
+
+        top_nodes = sorted(G.nodes(), key=lambda n: G.in_degree(n), reverse=True)[:25]
+
+        titles_above_threshold = {}
+        for paper_id in top_nodes:
+            full_title = self.semantic_scholar_api.get_paper_title(paper_id)
+            if full_title:
+                display_title = technique_to_title.get(full_title, full_title)
+                wrapped_title = self.wrap_text(display_title, 10)
+                titles_above_threshold[paper_id] = wrapped_title
+
+        node_sizes = [((G.in_degree(node) * 300)+1000) for node in top_nodes]
+
+        font_sizes = {node: G.in_degree(node) * 0.06 + 14 for node in top_nodes}
+
+        plt.figure(figsize=(50, 30))
+
+        pos = nx.spring_layout(G, k=.3, iterations=50, scale=2)
+        self.adjust_overlap(pos, top_nodes, min_dist=1, max_dist=7.5)
+
+        nx.draw_networkx_nodes(G, pos, nodelist=top_nodes, node_size=node_sizes, node_color=(45/255, 137/255, 145/255, 1))
+
+        nx.draw_networkx_edges(G, pos, edge_color="gray", width=0.3)
+
+        for node, label in titles_above_threshold.items():
+            x, y = pos[node]
+            plt.text(x, y, label, fontsize=font_sizes[node], ha="center", va="center", wrap=True)
+
+        plt.axis("off")
+        plt.show()
+
+    def visualize_citation_counts(self, paper_references, title_to_technique):
+        citation_counts = {}
+
+        for title, technique in title_to_technique.items():
+            paper_id = self.semantic_scholar_api.query_paper_id(title)
+            citation_count = sum(paper_id in refs for refs in paper_references.values())
+            citation_counts[technique] = citation_count
+
+        sorted_citations = sorted(citation_counts.items(), key=lambda x: x[1], reverse=True)
+
+        sorted_techniques, sorted_counts = zip(*sorted_citations)
+
+        plt.figure(figsize=(30, 12))
+        plt.bar(sorted_techniques, sorted_counts, color=(45 / 255, 137 / 255, 145 / 255, 1))
+
+        plt.yscale("log")
+
+        plt.xticks(rotation=45, ha="right")
+
+        ax = plt.gca()
+        ax.spines["top"].set_visible(False)
+        ax.spines["right"].set_visible(False)
+
+        plt.ylabel("Number of References")
+        plt.xlabel("Technique")
+
+        plt.tight_layout()
+        plt.show()
+
+
+class Main:
+    def __init__(self):
+        load_dotenv()
+        self.api_key = os.getenv("SEMANTIC_SCHOLAR_API_KEY")
+        self.paper_processor = PaperProcessor(self.api_key)
+        self.graph_visualizer = GraphVisualizer(self.api_key)
+
+    def process_papers(self, csv_file_path):
+        paper_references, unmatched_papers = self.paper_processor.process_papers(csv_file_path)
+        with open("revised_paper_references.json", "w") as json_file:
+            json.dump(paper_references, json_file, indent=4)
+        print("Data saved to revised_paper_references.json")
+
+    def process_additional_papers(self, titles):
+        paper_references, unmatched_titles = self.paper_processor.process_additional_papers(titles)
+        with open("paper_references_additional.json", "w") as json_file:
+            json.dump(paper_references, json_file, indent=4)
+        print("Data saved to paper_references_additional.json")
+        with open("unmatched_titles_additional.json", "w") as json_file:
+            json.dump(unmatched_titles, json_file, indent=4)
+        print("Data saved to unmatched_titles_additional.json")
+
+    def merge_paper_references(self):
+        with open("revised_paper_references.json", "r") as file:
+            paper_references = json.load(file)
+        with open("paper_references_additional.json", "r") as file:
+            paper_references_additional = json.load(file)
+        paper_references.update(paper_references_additional)
+        with open("complete_paper_references.json", "w") as file:
+            json.dump(paper_references, file, indent=4)
+        print("Merged data saved to complete_paper_references.json")
+
+    def clean_paper_references(self):
+        with open("complete_paper_references.json","r",) as file:
+            merged_paper_references = json.load(file)
+        for paper_id, references in merged_paper_references.items():
+            merged_paper_references[paper_id] = [ref for ref in references if ref in merged_paper_references]
+        with open("cleaned_complete_paper_references.json", "w") as file:
+            json.dump(merged_paper_references, file, indent=4)
+        print("Cleaned data saved to cleaned_merged_paper_references.json")
+
+    def visualize_graph(self, technique_to_title):
+        with open("src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json","r",) as json_file:
+            paper_references = json.load(json_file)
+        self.graph_visualizer.visualize_graph(paper_references, technique_to_title)
+
+if __name__ == "__main__":
+    main = Main()
+
+    titles = [
+        "Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints",
+        "Language Models are Few-Shot Learners",
+        "A Survey on In-context Learning",
+        "What Makes Good In-Context Examples for GPT-3?",
+        "Finding Support Examples for In-Context Learning",
+        "Unified Demonstration Retriever for In-Context Learning",
+        "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity",
+        "Reordering Examples Helps during Priming-based Few-Shot Learning",
+        "Learning To Retrieve Prompts for In-Context Learning",
+        "Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator",
+        "Large Language Models are Zero-Shot Reasoners",
+        "Large Language Models Are Human-Level Prompt Engineers",
+        "Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models",
+        "Thread of Thought Unraveling Chaotic Contexts",
+        "When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment",
+        "Automatic Chain of Thought Prompting in Large Language Models",
+        "True Detective: A Deep Abductive Reasoning Benchmark Undoable for GPT-3 and Challenging for GPT-4",
+        "Contrastive Chain-of-Thought Prompting",
+        "Gemini: A Family of Highly Capable Multimodal Models",
+        "Complexity-Based Prompting for Multi-Step Reasoning",
+        "Active Prompting with Chain-of-Thought for Large Language Models",
+        "MoT: Memory-of-Thought Enables ChatGPT to Self-Improve",
+        "Measuring and Narrowing the Compositionality Gap in Language Models",
+        "Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data",
+        "Tab-CoT: Zero-shot Tabular Chain of Thought",
+        "Is a Question Decomposition Unit All We Need?",
+        "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models",
+        "Decomposed Prompting: A Modular Approach for Solving Complex Tasks",
+        "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models",
+        "Tree of Thoughts: Deliberate Problem Solving with Large Language Models",
+        "Large Language Model Guided Tree-of-Thought",
+        "Cumulative Reasoning with Large Language Models",
+        "Graph of thoughts: Solving elaborate problems with large language models",
+        "Recursion of Thought: A Divide-and-Conquer Approach to Multi-Context Reasoning with Language Models",
+        "Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks",
+        "Faithful Chain-of-Thought Reasoning",
+        "Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding",
+        "Exploring Demonstration Ensembling for In-context Learning",
+        "$k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference",
+        "An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels",
+        "Self-Consistency Improves Chain of Thought Reasoning in Language Models",
+        "Universal Self-Consistency for Large Language Model Generation",
+        "Making Language Models Better Reasoners with Step-Aware Verifier",
+        "Language Models (Mostly) Know What They Know",
+        "Self-Refine: Iterative Refinement with Self-Feedback",
+        "RCOT: Detecting and Rectifying Factual Inconsistency in Reasoning by Reversing Chain-of-Thought",
+        "Large Language Models are Better Reasoners with Self-Verification",
+        "Deductive Verification of Chain-of-Thought Reasoning",
+        "Chain-of-Verification Reduces Hallucination in Large Language Models",
+        "Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations",
+        "Large Language Models Understand and Can be Enhanced by Emotional Stimuli",
+        "Re-Reading Improves Reasoning in Language Models",
+        "Think Twice: Perspective-Taking Improves Large Language Models' Theory-of-Mind Capabilities",
+        "Better Zero-Shot Reasoning with Self-Adaptive Prompting",
+        "Universal Self-Adaptive Prompting",
+        "System 2 Attention (is something you might need too)",
+        "Large Language Models as Optimizers",
+        "Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves",
+    ]
+
+    technique_to_title = {
+    "Language Models are Few-Shot Learners": "Few-Shot Learning",
+    "A Survey on In-context Learning": "In-context Learning Survey",
+    "Exploring Demonstration Ensembling for In-context Learning": "Demonstration Ensembling",
+    "Unified Demonstration Retriever for In-Context Learning": "Unified Demo Retriever",
+    "Finding Support Examples for In-Context Learning": "Support Examples",
+    "Large Language Models Are Human-Level Prompt Engineers": "Human-Level Prompting",
+    "Measuring and Narrowing the Compositionality Gap in Language Models": "Compositionality Gap",
+    "Automatic Chain of Thought Prompting in Large Language Models": "Automatic CoT",
+    "Complexity-Based Prompting for Multi-Step Reasoning": "Complexity-Based Prompting",
+    "Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator": "Self-Generated ICL",
+    "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models": "Least-to-Most Prompting",
+    "Learning To Retrieve Prompts for In-Context Learning": "Prompt Retrieval",
+    "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity": "Prompt Order Sensitivity",
+    "What Makes Good In-Context Examples for GPT-3?": "Good In-Context Examples",
+    "MoT: Memory-of-Thought Enables ChatGPT to Self-Improve": "Memory-of-Thought",
+    "kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference": "kNN Prompting",
+    "Large Language Models are Zero-Shot Reasoners": "Zero-Shot Reasoning",
+    "Self-Consistency Improves Chain of Thought Reasoning in Language Models": "Self-Consistency",
+    "Large Language Models as Optimizers": "LLMs as Optimizers",
+    "Decomposed Prompting: A Modular Approach for Solving Complex Tasks": "Decomposed Prompting",
+    "Is a Question Decomposition Unit All We Need?": "Question Decomposition",
+    "Deductive Verification of Chain-of-Thought Reasoning": "Deductive Verification",
+    "Active Prompting with Chain-of-Thought for Large Language Models": "Active Prompting",
+    "Large Language Model Guided Tree-of-Thought": "LLM Guided ToT",
+    "Language Models (Mostly) Know What They Know": "LLM Self-Knowledge",
+    "Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data": "Automatic Prompt Augmentation",
+    "Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations": "Maieutic Prompting",
+    "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models": "Plan-and-Solve Prompting",
+    "Tree of Thoughts: Deliberate Problem Solving with Large Language Models": "Tree of Thoughts",
+    "Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks": "Program of Thoughts",
+    "Self-Refine: Iterative Refinement with Self-Feedback": "Self-Refine",
+    "Cumulative Reasoning with Large Language Models": "Cumulative Reasoning",
+    "Faithful Chain-of-Thought Reasoning": "Faithful CoT",
+    "Making Language Models Better Reasoners with Step-Aware Verifier": "Step-Aware Verification",
+    "Graph of Thoughts: Solving Elaborate Problems with Large Language Models": "Graph of Thoughts",
+    "Chain-of-Verification Reduces Hallucination in Large Language Models": "Chain-of-Verification",
+    "Better Zero-Shot Reasoning with Self-Adaptive Prompting": "Self-Adaptive Prompting",
+    "Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves": "Rephrase and Respond"
+}
+    csv_file_path = "data/master_papers.csv"
+    main.process_papers(csv_file_path)
+    main.visualize_graph(technique_to_title)
\ No newline at end of file

From 5a2b9b9aec3a057510bdd2154b3fb67ec5ec1541 Mon Sep 17 00:00:00 2001
From: Sander Schulhoff <sanderschulhoff@gmail.com>
Date: Mon, 29 Apr 2024 14:39:13 -0400
Subject: [PATCH 11/19] chore: black

---
 .pre-commit-config.yaml                       |   3 +-
 .../experiments/graph_internal_references.py  | 178 ++++++++++++------
 .../get_papers/paperSource.py                 |   6 +-
 .../get_papers/semantic_scholar_source.py     |  14 +-
 4 files changed, 129 insertions(+), 72 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 7ac9b1e..ebfaa8f 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -2,4 +2,5 @@ repos:
 -   repo: https://github.com/psf/black
     rev: 23.10.1
     hooks:
-    -   id: black
\ No newline at end of file
+    -   id: black
+        exclude: src/prompt_systematic_review/experiments/find_internal_reference_count.py
\ No newline at end of file
diff --git a/src/prompt_systematic_review/experiments/graph_internal_references.py b/src/prompt_systematic_review/experiments/graph_internal_references.py
index 825e78a..aad2472 100644
--- a/src/prompt_systematic_review/experiments/graph_internal_references.py
+++ b/src/prompt_systematic_review/experiments/graph_internal_references.py
@@ -13,6 +13,7 @@
 import textwrap
 import matplotlib
 
+
 class SemanticScholarAPI:
     def __init__(self, api_key):
         self.api_key = api_key
@@ -56,8 +57,8 @@ def get_references(self, paper_id):
         return []
 
     def get_citation_counts(self, paper_ids):
-        url = 'https://api.semanticscholar.org/graph/v1/paper/batch'
-        params = {'fields': 'citationCount'}
+        url = "https://api.semanticscholar.org/graph/v1/paper/batch"
+        params = {"fields": "citationCount"}
         payload = {"ids": paper_ids}
         response = requests.post(url, params=params, json=payload)
         citation_counts = {}
@@ -65,8 +66,8 @@ def get_citation_counts(self, paper_ids):
         if response.status_code == 200:
             data = response.json()
             for paper in data:
-                paper_id = paper.get('paperId')
-                citation_count = paper.get('citationCount', 0)
+                paper_id = paper.get("paperId")
+                citation_count = paper.get("citationCount", 0)
                 citation_counts[paper_id] = citation_count
         else:
             print(f"Failed to fetch data: {response.status_code}")
@@ -142,10 +143,16 @@ def process_papers(self, csv_file_path):
                     if source == "Semantic Scholar":
                         paper_id = row.get("paperId", "").strip()
                     elif source == "arXiv":
-                        arxiv_paper_id = self.extract_arxiv_id(row.get("url", "").strip())
-                        paper_id = self.convert_arxiv_id_to_semantic_scholar_id(arxiv_paper_id)
+                        arxiv_paper_id = self.extract_arxiv_id(
+                            row.get("url", "").strip()
+                        )
+                        paper_id = self.convert_arxiv_id_to_semantic_scholar_id(
+                            arxiv_paper_id
+                        )
                     else:
-                        unmatched_papers[row.get("title", "").strip()] = "Source not supported"
+                        unmatched_papers[row.get("title", "").strip()] = (
+                            "Source not supported"
+                        )
                         continue
 
                     if paper_id:
@@ -153,7 +160,9 @@ def process_papers(self, csv_file_path):
                         if references is not None:
                             paper_references[paper_id] = references
                         else:
-                            unmatched_papers[row["title"]] = "No references found or error occurred"
+                            unmatched_papers[row["title"]] = (
+                                "No references found or error occurred"
+                            )
                     else:
                         print(f"Paper Id Could not be found for: {row}")
         else:
@@ -181,7 +190,16 @@ class GraphVisualizer:
     def __init__(self, api_key):
         self.semantic_scholar_api = SemanticScholarAPI(api_key)
 
-    def adjust_overlap(self, pos, nodes_to_adjust, min_dist=1.0, max_dist=2.0, repulsion_factor=1.05, attraction_factor=0.95, vertical_bias=2.0):
+    def adjust_overlap(
+        self,
+        pos,
+        nodes_to_adjust,
+        min_dist=1.0,
+        max_dist=2.0,
+        repulsion_factor=1.05,
+        attraction_factor=0.95,
+        vertical_bias=2.0,
+    ):
         for _ in range(1000):
             adjusted = False
             for node1 in nodes_to_adjust:
@@ -204,7 +222,10 @@ def adjust_overlap(self, pos, nodes_to_adjust, min_dist=1.0, max_dist=2.0, repul
                         dx, dy = 1, 0
                         dist = 1
 
-                    dx, dy = dx / dist * min_dist * factor, dy / dist * min_dist * factor
+                    dx, dy = (
+                        dx / dist * min_dist * factor,
+                        dy / dist * min_dist * factor,
+                    )
                     dy *= vertical_bias
 
                     pos[node1] = (x1 + dx, y1 + dy)
@@ -227,7 +248,9 @@ def visualize_graph(self, paper_references, technique_to_title):
             for ref_id in references:
                 G.add_edge(paper_id, ref_id)
 
-        nodes_with_incoming_edges = [node for node in G.nodes() if G.in_degree(node) > 0]
+        nodes_with_incoming_edges = [
+            node for node in G.nodes() if G.in_degree(node) > 0
+        ]
 
         top_nodes = sorted(G.nodes(), key=lambda n: G.in_degree(n), reverse=True)[:25]
 
@@ -239,22 +262,36 @@ def visualize_graph(self, paper_references, technique_to_title):
                 wrapped_title = self.wrap_text(display_title, 10)
                 titles_above_threshold[paper_id] = wrapped_title
 
-        node_sizes = [((G.in_degree(node) * 300)+1000) for node in top_nodes]
+        node_sizes = [((G.in_degree(node) * 300) + 1000) for node in top_nodes]
 
         font_sizes = {node: G.in_degree(node) * 0.06 + 14 for node in top_nodes}
 
         plt.figure(figsize=(50, 30))
 
-        pos = nx.spring_layout(G, k=.3, iterations=50, scale=2)
+        pos = nx.spring_layout(G, k=0.3, iterations=50, scale=2)
         self.adjust_overlap(pos, top_nodes, min_dist=1, max_dist=7.5)
 
-        nx.draw_networkx_nodes(G, pos, nodelist=top_nodes, node_size=node_sizes, node_color=(45/255, 137/255, 145/255, 1))
+        nx.draw_networkx_nodes(
+            G,
+            pos,
+            nodelist=top_nodes,
+            node_size=node_sizes,
+            node_color=(45 / 255, 137 / 255, 145 / 255, 1),
+        )
 
         nx.draw_networkx_edges(G, pos, edge_color="gray", width=0.3)
 
         for node, label in titles_above_threshold.items():
             x, y = pos[node]
-            plt.text(x, y, label, fontsize=font_sizes[node], ha="center", va="center", wrap=True)
+            plt.text(
+                x,
+                y,
+                label,
+                fontsize=font_sizes[node],
+                ha="center",
+                va="center",
+                wrap=True,
+            )
 
         plt.axis("off")
         plt.show()
@@ -267,12 +304,16 @@ def visualize_citation_counts(self, paper_references, title_to_technique):
             citation_count = sum(paper_id in refs for refs in paper_references.values())
             citation_counts[technique] = citation_count
 
-        sorted_citations = sorted(citation_counts.items(), key=lambda x: x[1], reverse=True)
+        sorted_citations = sorted(
+            citation_counts.items(), key=lambda x: x[1], reverse=True
+        )
 
         sorted_techniques, sorted_counts = zip(*sorted_citations)
 
         plt.figure(figsize=(30, 12))
-        plt.bar(sorted_techniques, sorted_counts, color=(45 / 255, 137 / 255, 145 / 255, 1))
+        plt.bar(
+            sorted_techniques, sorted_counts, color=(45 / 255, 137 / 255, 145 / 255, 1)
+        )
 
         plt.yscale("log")
 
@@ -297,13 +338,17 @@ def __init__(self):
         self.graph_visualizer = GraphVisualizer(self.api_key)
 
     def process_papers(self, csv_file_path):
-        paper_references, unmatched_papers = self.paper_processor.process_papers(csv_file_path)
+        paper_references, unmatched_papers = self.paper_processor.process_papers(
+            csv_file_path
+        )
         with open("revised_paper_references.json", "w") as json_file:
             json.dump(paper_references, json_file, indent=4)
         print("Data saved to revised_paper_references.json")
 
     def process_additional_papers(self, titles):
-        paper_references, unmatched_titles = self.paper_processor.process_additional_papers(titles)
+        paper_references, unmatched_titles = (
+            self.paper_processor.process_additional_papers(titles)
+        )
         with open("paper_references_additional.json", "w") as json_file:
             json.dump(paper_references, json_file, indent=4)
         print("Data saved to paper_references_additional.json")
@@ -322,19 +367,28 @@ def merge_paper_references(self):
         print("Merged data saved to complete_paper_references.json")
 
     def clean_paper_references(self):
-        with open("complete_paper_references.json","r",) as file:
+        with open(
+            "complete_paper_references.json",
+            "r",
+        ) as file:
             merged_paper_references = json.load(file)
         for paper_id, references in merged_paper_references.items():
-            merged_paper_references[paper_id] = [ref for ref in references if ref in merged_paper_references]
+            merged_paper_references[paper_id] = [
+                ref for ref in references if ref in merged_paper_references
+            ]
         with open("cleaned_complete_paper_references.json", "w") as file:
             json.dump(merged_paper_references, file, indent=4)
         print("Cleaned data saved to cleaned_merged_paper_references.json")
 
     def visualize_graph(self, technique_to_title):
-        with open("src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json","r",) as json_file:
+        with open(
+            "src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json",
+            "r",
+        ) as json_file:
             paper_references = json.load(json_file)
         self.graph_visualizer.visualize_graph(paper_references, technique_to_title)
 
+
 if __name__ == "__main__":
     main = Main()
 
@@ -400,45 +454,45 @@ def visualize_graph(self, technique_to_title):
     ]
 
     technique_to_title = {
-    "Language Models are Few-Shot Learners": "Few-Shot Learning",
-    "A Survey on In-context Learning": "In-context Learning Survey",
-    "Exploring Demonstration Ensembling for In-context Learning": "Demonstration Ensembling",
-    "Unified Demonstration Retriever for In-Context Learning": "Unified Demo Retriever",
-    "Finding Support Examples for In-Context Learning": "Support Examples",
-    "Large Language Models Are Human-Level Prompt Engineers": "Human-Level Prompting",
-    "Measuring and Narrowing the Compositionality Gap in Language Models": "Compositionality Gap",
-    "Automatic Chain of Thought Prompting in Large Language Models": "Automatic CoT",
-    "Complexity-Based Prompting for Multi-Step Reasoning": "Complexity-Based Prompting",
-    "Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator": "Self-Generated ICL",
-    "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models": "Least-to-Most Prompting",
-    "Learning To Retrieve Prompts for In-Context Learning": "Prompt Retrieval",
-    "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity": "Prompt Order Sensitivity",
-    "What Makes Good In-Context Examples for GPT-3?": "Good In-Context Examples",
-    "MoT: Memory-of-Thought Enables ChatGPT to Self-Improve": "Memory-of-Thought",
-    "kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference": "kNN Prompting",
-    "Large Language Models are Zero-Shot Reasoners": "Zero-Shot Reasoning",
-    "Self-Consistency Improves Chain of Thought Reasoning in Language Models": "Self-Consistency",
-    "Large Language Models as Optimizers": "LLMs as Optimizers",
-    "Decomposed Prompting: A Modular Approach for Solving Complex Tasks": "Decomposed Prompting",
-    "Is a Question Decomposition Unit All We Need?": "Question Decomposition",
-    "Deductive Verification of Chain-of-Thought Reasoning": "Deductive Verification",
-    "Active Prompting with Chain-of-Thought for Large Language Models": "Active Prompting",
-    "Large Language Model Guided Tree-of-Thought": "LLM Guided ToT",
-    "Language Models (Mostly) Know What They Know": "LLM Self-Knowledge",
-    "Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data": "Automatic Prompt Augmentation",
-    "Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations": "Maieutic Prompting",
-    "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models": "Plan-and-Solve Prompting",
-    "Tree of Thoughts: Deliberate Problem Solving with Large Language Models": "Tree of Thoughts",
-    "Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks": "Program of Thoughts",
-    "Self-Refine: Iterative Refinement with Self-Feedback": "Self-Refine",
-    "Cumulative Reasoning with Large Language Models": "Cumulative Reasoning",
-    "Faithful Chain-of-Thought Reasoning": "Faithful CoT",
-    "Making Language Models Better Reasoners with Step-Aware Verifier": "Step-Aware Verification",
-    "Graph of Thoughts: Solving Elaborate Problems with Large Language Models": "Graph of Thoughts",
-    "Chain-of-Verification Reduces Hallucination in Large Language Models": "Chain-of-Verification",
-    "Better Zero-Shot Reasoning with Self-Adaptive Prompting": "Self-Adaptive Prompting",
-    "Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves": "Rephrase and Respond"
-}
+        "Language Models are Few-Shot Learners": "Few-Shot Learning",
+        "A Survey on In-context Learning": "In-context Learning Survey",
+        "Exploring Demonstration Ensembling for In-context Learning": "Demonstration Ensembling",
+        "Unified Demonstration Retriever for In-Context Learning": "Unified Demo Retriever",
+        "Finding Support Examples for In-Context Learning": "Support Examples",
+        "Large Language Models Are Human-Level Prompt Engineers": "Human-Level Prompting",
+        "Measuring and Narrowing the Compositionality Gap in Language Models": "Compositionality Gap",
+        "Automatic Chain of Thought Prompting in Large Language Models": "Automatic CoT",
+        "Complexity-Based Prompting for Multi-Step Reasoning": "Complexity-Based Prompting",
+        "Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator": "Self-Generated ICL",
+        "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models": "Least-to-Most Prompting",
+        "Learning To Retrieve Prompts for In-Context Learning": "Prompt Retrieval",
+        "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity": "Prompt Order Sensitivity",
+        "What Makes Good In-Context Examples for GPT-3?": "Good In-Context Examples",
+        "MoT: Memory-of-Thought Enables ChatGPT to Self-Improve": "Memory-of-Thought",
+        "kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference": "kNN Prompting",
+        "Large Language Models are Zero-Shot Reasoners": "Zero-Shot Reasoning",
+        "Self-Consistency Improves Chain of Thought Reasoning in Language Models": "Self-Consistency",
+        "Large Language Models as Optimizers": "LLMs as Optimizers",
+        "Decomposed Prompting: A Modular Approach for Solving Complex Tasks": "Decomposed Prompting",
+        "Is a Question Decomposition Unit All We Need?": "Question Decomposition",
+        "Deductive Verification of Chain-of-Thought Reasoning": "Deductive Verification",
+        "Active Prompting with Chain-of-Thought for Large Language Models": "Active Prompting",
+        "Large Language Model Guided Tree-of-Thought": "LLM Guided ToT",
+        "Language Models (Mostly) Know What They Know": "LLM Self-Knowledge",
+        "Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data": "Automatic Prompt Augmentation",
+        "Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations": "Maieutic Prompting",
+        "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models": "Plan-and-Solve Prompting",
+        "Tree of Thoughts: Deliberate Problem Solving with Large Language Models": "Tree of Thoughts",
+        "Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks": "Program of Thoughts",
+        "Self-Refine: Iterative Refinement with Self-Feedback": "Self-Refine",
+        "Cumulative Reasoning with Large Language Models": "Cumulative Reasoning",
+        "Faithful Chain-of-Thought Reasoning": "Faithful CoT",
+        "Making Language Models Better Reasoners with Step-Aware Verifier": "Step-Aware Verification",
+        "Graph of Thoughts: Solving Elaborate Problems with Large Language Models": "Graph of Thoughts",
+        "Chain-of-Verification Reduces Hallucination in Large Language Models": "Chain-of-Verification",
+        "Better Zero-Shot Reasoning with Self-Adaptive Prompting": "Self-Adaptive Prompting",
+        "Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves": "Rephrase and Respond",
+    }
     csv_file_path = "data/master_papers.csv"
     main.process_papers(csv_file_path)
-    main.visualize_graph(technique_to_title)
\ No newline at end of file
+    main.visualize_graph(technique_to_title)
diff --git a/src/prompt_systematic_review/get_papers/paperSource.py b/src/prompt_systematic_review/get_papers/paperSource.py
index 8d529a0..8bbccd7 100644
--- a/src/prompt_systematic_review/get_papers/paperSource.py
+++ b/src/prompt_systematic_review/get_papers/paperSource.py
@@ -56,9 +56,9 @@ def to_dict(self):
             "title": self.title,
             "authors": self.authors,
             "url": self.url,
-            "dateSubmitted": self.dateSubmitted.isoformat()
-            if self.dateSubmitted
-            else None,
+            "dateSubmitted": (
+                self.dateSubmitted.isoformat() if self.dateSubmitted else None
+            ),
             "keyWords": self.keywords,
             "abstract": self.abstract,
             "paperId": self.paperId,
diff --git a/src/prompt_systematic_review/get_papers/semantic_scholar_source.py b/src/prompt_systematic_review/get_papers/semantic_scholar_source.py
index 5d16756..05d4fc0 100644
--- a/src/prompt_systematic_review/get_papers/semantic_scholar_source.py
+++ b/src/prompt_systematic_review/get_papers/semantic_scholar_source.py
@@ -50,12 +50,14 @@ def getPapers(self, count: int, key_words: List[str]) -> List[Paper]:
                         )
                         paper = Paper(
                             title=paper_data["title"],
-                            authors=[
-                                author["name"]
-                                for author in paper_data.get("authors", [])
-                            ]
-                            if paper_data["authors"]
-                            else "",
+                            authors=(
+                                [
+                                    author["name"]
+                                    for author in paper_data.get("authors", [])
+                                ]
+                                if paper_data["authors"]
+                                else ""
+                            ),
                             url=open_access_pdf_url,
                             dateSubmitted=publication_date,
                             keyWords=None,

From 0c0deb66fda9c0e30a8f5e542e578d55f5000220 Mon Sep 17 00:00:00 2001
From: Sander Schulhoff <sanderschulhoff@gmail.com>
Date: Mon, 29 Apr 2024 14:47:50 -0400
Subject: [PATCH 12/19] chore: update req

---
 requirements.txt | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index de78548..395ffb6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -18,6 +18,8 @@ load_dotenv
 tomotopy
 wordcloud
 pytest-dotenv
+python-dotenv
+networkx
 scipy
 networkx
 tomotopy
@@ -33,4 +35,5 @@ anytree==2.12.1
 urllib3<2
 acl-anthology-py
 seaborn
+requests
 -e .
\ No newline at end of file

From 66bbbf7d7f240b8ddb8a7703075c95cf8afbbe8f Mon Sep 17 00:00:00 2001
From: Sander Schulhoff <sanderschulhoff@gmail.com>
Date: Mon, 29 Apr 2024 14:48:03 -0400
Subject: [PATCH 13/19] chore: remove outdated file

---
 .../find_internal_reference_count.py          | 622 ------------------
 1 file changed, 622 deletions(-)
 delete mode 100644 src/prompt_systematic_review/experiments/find_internal_reference_count.py

diff --git a/src/prompt_systematic_review/experiments/find_internal_reference_count.py b/src/prompt_systematic_review/experiments/find_internal_reference_count.py
deleted file mode 100644
index bebfe5c..0000000
--- a/src/prompt_systematic_review/experiments/find_internal_reference_count.py
+++ /dev/null
@@ -1,622 +0,0 @@
-#%% Pip Installs
-%pip install tqdm
-%pip install pandas
-%pip install scipy
-
-# %% RUN 1st
-import os
-import requests
-import time
-import json
-from tqdm import tqdm
-import pandas as pd
-import os
-from dotenv import load_dotenv
-
-load_dotenv()
-api_key = os.getenv("SEMANTIC_SCHOLAR_API_KEY")
-# %% RUN 2nd
-def rate_limited_request(url, headers, max_retries=3, delay=0.1):
-    retries = 0
-    while retries < max_retries:
-        try:
-            time.sleep(delay)
-            response = requests.get(url, headers=headers)
-            response.raise_for_status()
-            return response
-        except requests.exceptions.HTTPError as e:
-            if response.status_code == 429 or response.status_code == 504:
-                print(f"Received status code {response.status_code}. Retrying...")
-                delay *= 2  # Exponential backoff
-                retries += 1
-            else:
-                print(f"HTTPError: {e}")
-                break  # For other HTTP errors, don't retry
-        except requests.RequestException as e:
-            print(f"Request error: {e}")
-            break  # For non-HTTP errors, don't retry
-    return None
-
-
-# Function to get references from semantic scholar Id
-def get_references(paper_id, api_key):
-    url = f"https://api.semanticscholar.org/graph/v1/paper/{paper_id}/references?fields=title,authors&limit=1000"
-    headers = {"x-api-key": api_key}
-    response = rate_limited_request(url, headers)
-
-    if response and response.status_code == 200:
-        data = response.json()
-        reference_ids = []
-
-        if "data" in data:
-            for ref in data["data"]:
-                if "citedPaper" in ref and "paperId" in ref["citedPaper"]:
-                    reference_ids.append(ref["citedPaper"]["paperId"])
-
-        return reference_ids
-    return []
-
-def get_citation_counts(paper_ids):
-    """
-    Fetches citation counts for a list of paper IDs from Semantic Scholar's API.
-
-    Parameters:
-    - paper_ids (list): A list of paper IDs (strings).
-
-    Returns:
-    - dict: A dictionary where keys are paper IDs and values are citation counts.
-    """
-    # Define the URL for the batch API endpoint
-    url = 'https://api.semanticscholar.org/graph/v1/paper/batch'
-    
-    # Define the parameters for the fields you want to retrieve
-    params = {'fields': 'citationCount'}
-    
-    # The payload for the POST request, including the list of paper IDs
-    payload = {"ids": paper_ids}
-    
-    # Make the POST request to the batch API endpoint
-    response = requests.post(url, params=params, json=payload)
-    
-    # Initialize an empty dictionary to store the citation counts
-    citation_counts = {}
-    
-    # Check if the request was successful
-    if response.status_code == 200:
-        # Parse the JSON response
-        data = response.json()
-        
-        # Loop through the papers in the response
-        for paper in data:
-            # Extract the paper ID and citation count
-            paper_id = paper.get('paperId')
-            citation_count = paper.get('citationCount', 0)
-            
-            # Add the citation count to the dictionary
-            citation_counts[paper_id] = citation_count
-    else:
-        print(f"Failed to fetch data: {response.status_code}")
-    
-    return citation_counts
-
-
-
-# Function to query paper title from Arxiv Id
-def get_arxiv_paper_title(arxiv_id):
-    arxiv_url = f"http://export.arxiv.org/api/query?id_list={arxiv_id}"
-    response = requests.get(arxiv_url)
-    if response.status_code != 200:
-        return None
-    start = response.text.find("<title>") + 7
-    end = response.text.find("</title>", start)
-    title = response.text[start:end].strip()
-    return title
-
-
-# Function to query Semantic Scholar for a paper ID using title
-def query_paper_id(title, api_key):
-    url = f"https://api.semanticscholar.org/graph/v1/paper/search?query={title}&limit=1"
-    headers = {"x-api-key": api_key}
-    response = rate_limited_request(url, headers, delay=1, max_retries=4)
-    if response.status_code == 200:
-        data = response.json()
-        if "data" in data and data["data"]:
-            return data["data"][0]["paperId"]
-    return None
-
-
-# Function to convert arXiv ID into Semantic Scholar ID
-def convert_arxiv_id_to_semantic_scholar_id(arxiv_id, api_key):
-    title = get_arxiv_paper_title(arxiv_id)
-    if title:
-        return query_paper_id(title, api_key)
-    return None
-
-
-# Function to get the title for a given paper ID
-def get_paper_title(paper_id, api_key):
-    url = f"https://api.semanticscholar.org/graph/v1/paper/{paper_id}"
-    headers = {"x-api-key": api_key}
-    response = rate_limited_request(url, headers)
-
-    if response and response.status_code == 200:
-        data = response.json()
-        return data.get("title")
-    else:
-        print(f"Failed to fetch data for paper ID: {paper_id}")
-        return None
-
-
-# %%Run 3rd
-import os
-import csv
-import json
-from tqdm import tqdm
-import random
-
-# Path to the CSV file containing the papers' data
-csv_file_path = "../../../data/master_papers.csv"
-
-# Dictionary to hold the references
-paper_references = {}
-unmatched_papers = {}
-
-
-# Function to extract Arxiv ID from the URL
-def extract_arxiv_id(url):
-    # Extract the part of the URL after 'pdf/' and before '.pdf'
-    base = url.rsplit("/", 1)[-1]
-    arxiv_id_with_version = base.split(".pdf")[0]
-    # Remove the version part if it exists (e.g., 'v1')
-    arxiv_id = (
-        arxiv_id_with_version.split("v")[0]
-        if "v" in arxiv_id_with_version
-        else arxiv_id_with_version
-    )
-    return arxiv_id
-
-
-if os.path.exists(csv_file_path):
-    with open(csv_file_path, mode="r", encoding="utf-8") as csvfile:
-        # Let csv.DictReader read the headers directly from the file
-        csv_reader = csv.DictReader(csvfile, delimiter=",")
-
-        # Convert the csv_reader to a list to allow for random sampling
-        all_papers = list(csv_reader)
-        # random_papers = random.sample(all_papers, min(20, len(all_papers)))  # Safely sample 20 papers or the total count if less
-        for row in tqdm(all_papers, desc="Processing Papers"):
-            source = row.get(
-                "source", ""
-            ).strip()  # Use .get() to avoid KeyError and strip() to remove any extra whitespace
-            if source == "Semantic Scholar":
-                paper_id = row.get("paperId", "").strip()  # Use .get() here as well
-            elif source == "arXiv":
-                arxiv_paper_id = extract_arxiv_id(row.get("url", "").strip())
-                paper_id = convert_arxiv_id_to_semantic_scholar_id(
-                    arxiv_paper_id, api_key=api_key
-                )
-            else:
-                unmatched_papers[row.get("title", "").strip()] = "Source not supported"
-                continue
-            # print(paper_id)
-            if paper_id:
-                references = get_references(paper_id, api_key=api_key)
-                if references is not None:
-                    paper_references[paper_id] = references
-                else:
-                    unmatched_papers[
-                        row["title"]
-                    ] = "No references found or error occurred"
-            else:
-                print(f"Paper Id Could not be found for: {row}")
-
-else:
-    print(f"CSV file does not exist: {csv_file_path}")
-
-# Save the results to JSON files
-with open("revised_paper_references.json", "w") as json_file:
-    json.dump(paper_references, json_file, indent=4)
-print("Data saved to revised_paper_references.json")
-
-# %%RUN 4th
-# Second main to add important papers not in our original dataset
-
-paper_references = {}
-unmatched_titles = {}
-
-titles = [
-    "Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints",
-    "Language Models are Few-Shot Learners",
-    "A Survey on In-context Learning",
-    "What Makes Good In-Context Examples for GPT-3?",
-    "Finding Support Examples for In-Context Learning",
-    "Unified Demonstration Retriever for In-Context Learning",
-    "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity",
-    "Reordering Examples Helps during Priming-based Few-Shot Learning",
-    "Learning To Retrieve Prompts for In-Context Learning",
-    "Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator",
-    "Large Language Models are Zero-Shot Reasoners",
-    "Large Language Models Are Human-Level Prompt Engineers",
-    "Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models",
-    "Thread of Thought Unraveling Chaotic Contexts",
-    "When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment",
-    "Automatic Chain of Thought Prompting in Large Language Models",
-    "True Detective: A Deep Abductive Reasoning Benchmark Undoable for GPT-3 and Challenging for GPT-4",
-    "Contrastive Chain-of-Thought Prompting",
-    "Gemini: A Family of Highly Capable Multimodal Models",
-    "Complexity-Based Prompting for Multi-Step Reasoning",
-    "Active Prompting with Chain-of-Thought for Large Language Models",
-    "MoT: Memory-of-Thought Enables ChatGPT to Self-Improve",
-    "Measuring and Narrowing the Compositionality Gap in Language Models",
-    "Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data",
-    "Tab-CoT: Zero-shot Tabular Chain of Thought",
-    "Is a Question Decomposition Unit All We Need?",
-    "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models",
-    "Decomposed Prompting: A Modular Approach for Solving Complex Tasks",
-    "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models",
-    "Tree of Thoughts: Deliberate Problem Solving with Large Language Models",
-    "Large Language Model Guided Tree-of-Thought",
-    "Cumulative Reasoning with Large Language Models",
-    "Graph of thoughts: Solving elaborate problems with large language models",
-    "Recursion of Thought: A Divide-and-Conquer Approach to Multi-Context Reasoning with Language Models",
-    "Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks",
-    "Faithful Chain-of-Thought Reasoning",
-    "Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding",
-    "Exploring Demonstration Ensembling for In-context Learning",
-    "$k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference",
-    "An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels",
-    "Self-Consistency Improves Chain of Thought Reasoning in Language Models",
-    "Universal Self-Consistency for Large Language Model Generation",
-    "Making Language Models Better Reasoners with Step-Aware Verifier",
-    "Language Models (Mostly) Know What They Know",
-    "Self-Refine: Iterative Refinement with Self-Feedback",
-    "RCOT: Detecting and Rectifying Factual Inconsistency in Reasoning by Reversing Chain-of-Thought",
-    "Large Language Models are Better Reasoners with Self-Verification",
-    "Deductive Verification of Chain-of-Thought Reasoning",
-    "Chain-of-Verification Reduces Hallucination in Large Language Models",
-    "Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations",
-    "Large Language Models Understand and Can be Enhanced by Emotional Stimuli",
-    "Re-Reading Improves Reasoning in Language Models",
-    "Think Twice: Perspective-Taking Improves Large Language Models' Theory-of-Mind Capabilities",
-    "Better Zero-Shot Reasoning with Self-Adaptive Prompting",
-    "Universal Self-Adaptive Prompting",
-    "System 2 Attention (is something you might need too)",
-    "Large Language Models as Optimizers",
-    "Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves",
-]
-
-# Processing the papers with a progress bar
-for title in tqdm(titles):
-    paper_id = query_paper_id(title, api_key)
-    references = get_references(paper_id, api_key)
-
-    if paper_id:
-        paper_references[paper_id] = references
-    else:
-        unmatched_titles[title] = "No matching paper ID found"
-
-# Save the results to JSON files
-with open("paper_references_additional.json", "w") as json_file:
-    json.dump(paper_references, json_file, indent=4)
-print("Data saved to paper_references_additional.json")
-
-with open("unmatched_titles_additional.json", "w") as json_file:
-    json.dump(unmatched_titles, json_file, indent=4)
-print("Data saved to unmatched_titles_additional.json")
-
-
-# %%Run 5th
-# Merge the two files
-
-# Load the existing data from both JSON files
-with open("revised_paper_references.json", "r") as file:
-    paper_references = json.load(file)
-
-with open("paper_references_additional.json", "r") as file:
-    paper_references_additional = json.load(file)
-
-
-# Merge the two dictionaries
-# If there are duplicate keys, the values from paper_references_additional will be used
-paper_references.update(paper_references_additional)
-
-# Save the merged data back to a JSON file
-with open("complete_paper_references.json", "w") as file:
-    json.dump(paper_references, file, indent=4)
-
-print("Merged data saved to complete_paper_references.json")
-
-
-# %%Run 6th
-# Only keep refernces which refer to papers in our combined dataset
-
-# Load the merged paper references
-with open("complete_paper_references.json", "r") as file:
-    merged_paper_references = json.load(file)
-
-# Filter the references so that only those that are keys in the dictionary are kept
-for paper_id, references in merged_paper_references.items():
-    merged_paper_references[paper_id] = [
-        ref for ref in references if ref in merged_paper_references
-    ]
-
-# Save the cleaned data back to a JSON file
-with open("cleaned_complete_paper_references.json", "w") as file:
-    json.dump(merged_paper_references, file, indent=4)
-
-print("Cleaned data saved to cleaned_merged_paper_references.json")
-
-
-# %%Run - Update File Path
-import scipy 
-import networkx as nx
-import matplotlib.pyplot as plt
-import textwrap
-
-
-# Function to adjust node positions to maintain a min and max distance
-def adjust_overlap(pos, nodes_to_adjust, min_dist=1.0, max_dist=2.0, repulsion_factor=1.05, attraction_factor=0.95, vertical_bias=2.0):
-    for _ in range(1000):  # Iteration limit
-        adjusted = False
-        for node1 in nodes_to_adjust:
-            for node2 in nodes_to_adjust:
-                if node1 == node2:
-                    continue  # Skip self comparisons
-                x1, y1 = pos[node1]
-                x2, y2 = pos[node2]
-                dx, dy = x1 - x2, y1 - y2
-                dist = (dx**2 + dy**2) ** 0.5  # Euclidean distance
-                
-                if dist < min_dist:  # Nodes too close, push apart
-                    factor = repulsion_factor
-                elif dist > max_dist:  # Nodes too far, pull together
-                    factor = -attraction_factor
-                else:
-                    continue  # Distance is acceptable, no adjustment
-                
-                if dist == 0:  # Prevent division by zero
-                    dx, dy = 1, 0
-                    dist = 1
-                
-                dx, dy = dx / dist * min_dist * factor, dy / dist * min_dist * factor
-                dy *= vertical_bias  # Apply vertical adjustment
-                
-                # Adjust positions
-                pos[node1] = (x1 + dx, y1 + dy)
-                pos[node2] = (x2 - dx, y2 - dy)
-                adjusted = True
-
-        if not adjusted:
-            break  # Stop if no adjustments were made in a full pass
-
-
-# Load the cleaned references
-with open("cleaned_complete_paper_references.json","r",
-) as json_file:
-    paper_references = json.load(json_file)
-
-# Create the graph
-G = nx.DiGraph()
-for paper_id, references in paper_references.items():
-    for ref_id in references:
-        G.add_edge(paper_id, ref_id)
-
-
-technique_to_title = {
-    "Language Models are Few-Shot Learners": "Few-Shot Learning",
-    "A Survey on In-context Learning": "In-context Learning Survey",
-    "Exploring Demonstration Ensembling for In-context Learning": "Demonstration Ensembling",
-    "Unified Demonstration Retriever for In-Context Learning": "Unified Demo Retriever",
-    "Finding Support Examples for In-Context Learning": "Support Examples",
-    "Large Language Models Are Human-Level Prompt Engineers": "Human-Level Prompting",
-    "Measuring and Narrowing the Compositionality Gap in Language Models": "Compositionality Gap",
-    "Automatic Chain of Thought Prompting in Large Language Models": "Automatic CoT",
-    "Complexity-Based Prompting for Multi-Step Reasoning": "Complexity-Based Prompting",
-    "Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator": "Self-Generated ICL",
-    "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models": "Least-to-Most Prompting",
-    "Learning To Retrieve Prompts for In-Context Learning": "Prompt Retrieval",
-    "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity": "Prompt Order Sensitivity",
-    "What Makes Good In-Context Examples for GPT-3?": "Good In-Context Examples",
-    "MoT: Memory-of-Thought Enables ChatGPT to Self-Improve": "Memory-of-Thought",
-    "kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference": "kNN Prompting",
-    "Large Language Models are Zero-Shot Reasoners": "Zero-Shot Reasoning",
-    "Self-Consistency Improves Chain of Thought Reasoning in Language Models": "Self-Consistency",
-    "Large Language Models as Optimizers": "LLMs as Optimizers",
-    "Decomposed Prompting: A Modular Approach for Solving Complex Tasks": "Decomposed Prompting",
-    "Is a Question Decomposition Unit All We Need?": "Question Decomposition",
-    "Deductive Verification of Chain-of-Thought Reasoning": "Deductive Verification",
-    "Active Prompting with Chain-of-Thought for Large Language Models": "Active Prompting",
-    "Large Language Model Guided Tree-of-Thought": "LLM Guided ToT",
-    "Language Models (Mostly) Know What They Know": "LLM Self-Knowledge",
-    "Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data": "Automatic Prompt Augmentation",
-    "Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations": "Maieutic Prompting",
-    "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models": "Plan-and-Solve Prompting",
-    "Tree of Thoughts: Deliberate Problem Solving with Large Language Models": "Tree of Thoughts",
-    "Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks": "Program of Thoughts",
-    "Self-Refine: Iterative Refinement with Self-Feedback": "Self-Refine",
-    "Cumulative Reasoning with Large Language Models": "Cumulative Reasoning",
-    "Faithful Chain-of-Thought Reasoning": "Faithful CoT",
-    "Making Language Models Better Reasoners with Step-Aware Verifier": "Step-Aware Verification",
-    "Graph of Thoughts: Solving Elaborate Problems with Large Language Models": "Graph of Thoughts",
-    "Chain-of-Verification Reduces Hallucination in Large Language Models": "Chain-of-Verification",
-    "Better Zero-Shot Reasoning with Self-Adaptive Prompting": "Self-Adaptive Prompting",
-    "Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves": "Rephrase and Respond"
-}
-# Find the nodes with at least one incoming edge
-nodes_with_incoming_edges = [node for node in G.nodes() if G.in_degree(node) > 0]
-
-top_nodes = sorted(G.nodes(), key=lambda n: G.in_degree(n), reverse=True)[:25]
-
-
-# Define a function to wrap text into at most two lines
-def wrap_text(text, width, max_lines=3):
-    wrapped_lines = textwrap.wrap(text, width)
-    if len(wrapped_lines) > max_lines:
-        wrapped_lines = wrapped_lines[:max_lines]
-        wrapped_lines[
-            -1
-        ] += "..."  # Add ellipsis to the last line to indicate truncation
-    return "\n".join(wrapped_lines)
-
-
-# Assign and label nodes with titles
-titles_above_threshold = {}
-for paper_id in top_nodes:
-    full_title = get_paper_title(paper_id, api_key)  # Fetch full paper title
-    if full_title:
-        display_title = technique_to_title.get(full_title, full_title)
-        wrapped_title = wrap_text(display_title, 10)
-        titles_above_threshold[paper_id] = wrapped_title
-
-# Set node sizes proportional to the number of incoming edges (increased size)
-node_sizes = [((G.in_degree(node) * 300)+1000) for node in top_nodes]
-
-# Calculate font size based on in-degree (you can adjust the scaling factor)
-font_sizes = {node: G.in_degree(node) * 0.06 + 14 for node in top_nodes}
-
-# Draw the graph with adjusted layout parameters
-plt.figure(figsize=(50, 30))
-
-pos = nx.spring_layout(G, k=.3, iterations=50, scale=2)  # Initial layout
-adjust_overlap(pos, top_nodes, min_dist=1, max_dist=7.5)  # Adjust node positions
-
-# Draw nodes with incoming edges
-nx.draw_networkx_nodes(
-    G, pos, nodelist=top_nodes, node_size=node_sizes, node_color=(45/255, 137/255, 145/255, 1)
-)
-
-# Draw the edges
-nx.draw_networkx_edges(G, pos, edge_color="gray", width=0.3)
-
-# Assign and label nodes with titles without y_offset
-for node, label in titles_above_threshold.items():
-    x, y = pos[node]
-    plt.text(x, y, label, fontsize=font_sizes[node], ha="center", va="center", wrap=True)
-
-plt.axis("off")
-plt.show()
-# # Print the names of all the nodes
-# print("Names of all nodes:")
-# for node in top_nodes:
-#     full_title = get_paper_title(node, api_key)
-#     if full_title:
-#         print(full_title)
-
-# %%Run 
-# Generate a graph of all the papers with more than 10 internal references
-import json
-import matplotlib.pyplot as plt
-import matplotlib
-
-# Set font properties globally
-matplotlib.rcParams.update({"font.family": "Arial", "font.size": 10})
-
-# Mapping of paper titles to their techniques
-title_to_technique = {
-    "A Practical Survey on Zero-Shot Prompt Design for In-Context Learning": "Zero-Shot Prompt",
-    "Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm": "Role Prompting",
-    "Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints": "Style Prompting",
-    "Language Models are Few-Shot Learners": "Few-shot Learning (FSL)",
-    # "A Survey on In-context Learning": "In-context learning (ICL)",
-    "What Makes Good In-Context Examples for {\GPT}-3?": "In-Context Learning (ICL)",
-    "Finding Support Examples for In-Context Learning": "fiLter-thEN-Search (LENS)",
-    "Unified Demonstration Retriever for In-Context Learning": "Unified Demonstration Retriever (UDR)",
-    "Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity": "Example Ordering",
-    "Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator": "Example Generation",
-    "Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?": "Example Label Quality",
-    "Selective Annotation Makes Language Models Better Few-Shot Learners": "Input Distribution",
-    "Chain-of-thought prompting elicits reasoning in large language models": "Chain-of-Thought",
-    "Large Language Models are Zero-Shot Reasoners": "Zero-shot-CoT",
-    "Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models": "Step-Back Prompting",
-    "Thread of Thought Unraveling Chaotic Contexts": "Thread-of-Thought (ThoT)",
-    "When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment": "Moral Chain-of-Thought",
-    "Automatic Chain of Thought Prompting in Large Language Models": "Few-Shot CoT",
-    "True Detective: A Deep Abductive Reasoning Benchmark Undoable for GPT-3 and Challenging for GPT-4": "Del_2023",
-    "Contrastive Chain-of-Thought Prompting": "Contrastive Chain of Thought",
-    "Gemini: A Family of Highly Capable Multimodal Models": "Uncertainty-Routed CoT",
-    "Complexity-Based Prompting for Multi-Step Reasoning": "Complexity-based Prompting",
-    "Active Prompting with Chain-of-Thought for Large Language Models": "Active-Prompt",
-    "MoT: Memory-of-Thought Enables ChatGPT to Self-Improve": "Memory-of-Thought",
-    "Measuring and Narrowing the Compositionality Gap in Language Models": "Self-Ask",
-    "Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data": "Automate-CoT",
-    "Tab-CoT: Zero-shot Tabular Chain of Thought": "Tab-CoT",
-    "Is a Question Decomposition Unit All We Need?": "Decomposition",
-    "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models": "Least-to-most Prompting",
-    "Decomposed Prompting: A Modular Approach for Solving Complex Tasks": "Decomposed Prompting (DECOMP)",
-    "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models": "Plan-and-Solve Prompting",
-    "Tree of Thoughts: Deliberate Problem Solving with Large Language Models": "Tree-of-Thought (ToT)",
-    "Cumulative Reasoning with Large Language Models": "Cumulative Reasoning",
-    "Graph of thoughts: Solving elaborate problems with large language models": "Graph-of-Thoughts",
-    "Recursion of Thought: A Divide-and-Conquer Approach to Multi-Context Reasoning with Language Models": "Recursion of Thought",
-    "Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks": "Program-of-Thoughts",
-    "Faithful Chain-of-Thought Reasoning": "Faithful Chain-of-Thought",
-    "Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding": "Skeleton-of-Thought",
-    "Exploring Demonstration Ensembling for In-context Learning": "Demonstration Ensembling (DENSE)",
-    "$k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference": "kNN Prompting",
-    "An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels": "Max Mutual Information Method",
-    "Self-Consistency Improves Chain of Thought Reasoning in Language Models": "Self-Consistency",
-    "Universal Self-Consistency for Large Language Model Generation": "Universal Self-Consistency",
-    "Making Language Models Better Reasoners with Step-Aware Verifier": "DiVeRSe",
-    "Language Models (Mostly) Know What They Know": "Self-Evaluation",
-    "Self-Refine: Iterative Refinement with Self-Feedback": "Self-Refine",
-    "RCOT: Detecting and Rectifying Factual Inconsistency in Reasoning by Reversing Chain-of-Thought": "Reversing Chain-of-Thought (RCoT)",
-    "Large Language Models are Better Reasoners with Self-Verification": "Self Verification",
-    "Deductive Verification of Chain-of-Thought Reasoning": "Deductive Verification",
-    "Chain-of-Verification Reduces Hallucination in Large Language Models": "Chain-of-Verification (COVE)",
-    "Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations": "Maieutic Prompting",
-    "How can we know what language models know?": "Prompt Mining",
-    "Large Language Models Understand and Can be Enhanced by Emotional Stimuli": "EmotionPrompt",
-    "Re-Reading Improves Reasoning in Language Models": "Re-reading (RE2)",
-    "Think Twice: Perspective-Taking Improves Large Language Models' Theory-of-Mind Capabilities": "Think Twice (SIMTOM)",
-    "Better Zero-Shot Reasoning with Self-Adaptive Prompting": "Consistency-based Self-adaptive Prompting (COSP)",
-    "Universal Self-Adaptive Prompting": "Universal Self-Adaptive Prompting (USP)",
-    "System 2 Attention (is something you might need too)": "System 2 Attention",
-    "Large Language Models as Optimizers": "OPRO",
-    "Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves": "Rephrase and Respond (RAR)",
-}
-
-# Load the existing dictionary of paper references
-with open(
-    "cleaned_complete_paper_references.json","r",) as file:
-    paper_references = json.load(file)
-
-# Initialize a dictionary for citation counts
-citation_counts = {}
-
-# Iterate over the title_to_technique mapping
-for title, technique in title_to_technique.items():
-    paper_id = query_paper_id(title, api_key)  # Get the paper ID for each title
-    # Count how many times each paper_id appears in the reference lists of other papers
-    citation_count = sum(paper_id in refs for refs in paper_references.values())
-    citation_counts[technique] = citation_count
-
-# Sort the citation counts in descending order
-sorted_citations = sorted(citation_counts.items(), key=lambda x: x[1], reverse=True)
-
-# Unpack the sorted data for plotting
-sorted_techniques, sorted_counts = zip(*sorted_citations)
-
-# Create a vertical bar chart
-plt.figure(figsize=(30, 12))
-plt.bar(
-    sorted_techniques, sorted_counts, color=(45 / 255, 137 / 255, 145 / 255, 1)
-)  # RGBA color
-
-plt.yscale("log")
-
-# Rotate the x-axis labels by 45 degrees for better readability
-plt.xticks(rotation=45, ha="right")
-
-# Remove the top and right borders
-ax = plt.gca()  # Get current axes
-ax.spines["top"].set_visible(False)
-ax.spines["right"].set_visible(False)
-
-# Add labels and title
-plt.ylabel("Number of References")
-plt.xlabel("Technique")
-# plt.title("Internal Citation Counts for Techniques Based on Papers")
-
-plt.tight_layout()  # Adjusts layout to prevent clipping of labels
-plt.show()
-# %%

From 5e4f1c903b412a9e364392114cfeb1590ada6352 Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Mon, 29 Apr 2024 17:06:15 -0400
Subject: [PATCH 14/19] updated main

---
 .../experiments/graph_internal_references.py                   | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/prompt_systematic_review/experiments/graph_internal_references.py b/src/prompt_systematic_review/experiments/graph_internal_references.py
index 825e78a..293d43d 100644
--- a/src/prompt_systematic_review/experiments/graph_internal_references.py
+++ b/src/prompt_systematic_review/experiments/graph_internal_references.py
@@ -441,4 +441,7 @@ def visualize_graph(self, technique_to_title):
 }
     csv_file_path = "data/master_papers.csv"
     main.process_papers(csv_file_path)
+    main.process_additional_papers(titles)
+    main.merge_paper_references
+    main.clean_paper_references()
     main.visualize_graph(technique_to_title)
\ No newline at end of file

From 5e9ad6b9637faaa857f2b0f22a058f014e62f4dc Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Tue, 30 Apr 2024 16:13:35 -0400
Subject: [PATCH 15/19] Updated Routes

---
 .../experiments/graph_internal_references.py      | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/src/prompt_systematic_review/experiments/graph_internal_references.py b/src/prompt_systematic_review/experiments/graph_internal_references.py
index dd93364..d8522f7 100644
--- a/src/prompt_systematic_review/experiments/graph_internal_references.py
+++ b/src/prompt_systematic_review/experiments/graph_internal_references.py
@@ -362,7 +362,7 @@ def merge_paper_references(self):
             paper_references = json.load(file)
         with open("paper_references_additional.json", "r") as file:
             paper_references_additional = json.load(file)
-        paper_references.update(paper_references_additional)
+        paper_references.update(paper_references_additional) 
         with open("complete_paper_references.json", "w") as file:
             json.dump(paper_references, file, indent=4)
         print("Merged data saved to complete_paper_references.json")
@@ -382,10 +382,7 @@ def clean_paper_references(self):
         print("Cleaned data saved to cleaned_merged_paper_references.json")
 
     def visualize_graph(self, technique_to_title):
-        with open(
-            "src/prompt_systematic_review/experiments/cleaned_complete_paper_references.json",
-            "r",
-        ) as json_file:
+        with open("cleaned_complete_paper_references.json", "r") as json_file:
             paper_references = json.load(json_file)
         self.graph_visualizer.visualize_graph(paper_references, technique_to_title)
 
@@ -494,9 +491,9 @@ def visualize_graph(self, technique_to_title):
         "Better Zero-Shot Reasoning with Self-Adaptive Prompting": "Self-Adaptive Prompting",
         "Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves": "Rephrase and Respond",
     }
-    csv_file_path = "data/master_papers.csv"
-    main.process_papers(csv_file_path)
-    main.process_additional_papers(titles)
-    main.merge_paper_references
+    # csv_file_path = "data/master_papers.csv"
+    # main.process_papers(csv_file_path)
+    # main.process_additional_papers(titles)
+    main.merge_paper_references()
     main.clean_paper_references()
     main.visualize_graph(technique_to_title)

From c6c268d0461d39984f5ce3a642a1a15e2dfe8f07 Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Wed, 1 May 2024 01:46:19 -0400
Subject: [PATCH 16/19] Will Save as PNG

---
 .../experiments/graph_internal_references.py       | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/src/prompt_systematic_review/experiments/graph_internal_references.py b/src/prompt_systematic_review/experiments/graph_internal_references.py
index d8522f7..8191319 100644
--- a/src/prompt_systematic_review/experiments/graph_internal_references.py
+++ b/src/prompt_systematic_review/experiments/graph_internal_references.py
@@ -262,14 +262,14 @@ def visualize_graph(self, paper_references, technique_to_title):
                 wrapped_title = self.wrap_text(display_title, 10)
                 titles_above_threshold[paper_id] = wrapped_title
 
-        node_sizes = [((G.in_degree(node) * 300) + 1000) for node in top_nodes]
+        node_sizes = [((G.in_degree(node) * 8) + 35) for node in top_nodes]
 
-        font_sizes = {node: G.in_degree(node) * 0.06 + 14 for node in top_nodes}
+        font_sizes = {node: G.in_degree(node) * 0.001 + 12 for node in top_nodes}
 
-        plt.figure(figsize=(50, 30))
+        plt.figure(figsize=(10, 6))
 
         pos = nx.spring_layout(G, k=0.3, iterations=50, scale=2)
-        self.adjust_overlap(pos, top_nodes, min_dist=1, max_dist=7.5)
+        self.adjust_overlap(pos, top_nodes, min_dist=.5, max_dist=5)
 
         nx.draw_networkx_nodes(
             G,
@@ -295,6 +295,8 @@ def visualize_graph(self, paper_references, technique_to_title):
 
         plt.axis("off")
         plt.show()
+        plt.savefig('network_graph.png', format='png', dpi=300) 
+
 
     def visualize_citation_counts(self, paper_references, title_to_technique):
         citation_counts = {}
@@ -494,6 +496,6 @@ def visualize_graph(self, technique_to_title):
     # csv_file_path = "data/master_papers.csv"
     # main.process_papers(csv_file_path)
     # main.process_additional_papers(titles)
-    main.merge_paper_references()
-    main.clean_paper_references()
+    # main.merge_paper_references()
+    # main.clean_paper_references()
     main.visualize_graph(technique_to_title)

From cc3fa176f149447620ae36162a7e060cb03ddb52 Mon Sep 17 00:00:00 2001
From: AayushGupta16 <aayugupta04@gmail.com>
Date: Wed, 1 May 2024 02:00:09 -0400
Subject: [PATCH 17/19] Added documentation

---
 .../experiments/graph_internal_references.py  | 34 +++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/src/prompt_systematic_review/experiments/graph_internal_references.py b/src/prompt_systematic_review/experiments/graph_internal_references.py
index 8191319..a464bee 100644
--- a/src/prompt_systematic_review/experiments/graph_internal_references.py
+++ b/src/prompt_systematic_review/experiments/graph_internal_references.py
@@ -14,6 +14,40 @@
 import matplotlib
 
 
+"""
+This script processes a CSV file containing research paper information, retrieves reference data from the Semantic Scholar API, 
+and visualizes the citation graph and citation counts.
+
+The SemanticScholarAPI class handles interactions with the Semantic Scholar API, including getting paper references and citation counts.
+It uses rate limiting and exponential backoff to handle API rate limits and errors.
+
+The ArxivAPI class retrieves paper titles from the arXiv API given an arXiv ID.
+
+The PaperProcessor class processes the CSV file, extracts paper IDs (either directly from Semantic Scholar or by converting arXiv IDs), 
+and retrieves the references for each paper using the SemanticScholarAPI. It can also process additional papers given a list of titles.
+
+The GraphVisualizer class visualizes the citation graph, showing the most referenced papers and their connections. 
+It applies techniques like adjusting node overlap, wrapping long titles, and scaling node sizes based on the number of incoming citations.
+It can also visualize a bar chart of the citation counts for different techniques.
+
+The Main class ties everything together. It loads the API key from a .env file, initializes the other classes, and provides methods to:
+1. Process the initial CSV file
+2. Process additional papers 
+3. Merge the reference data
+4. Clean the merged data (remove references to papers not in the dataset)
+5. Visualize the citation graph
+
+To use this script:
+1. Set your Semantic Scholar API key in a .env file as SEMANTIC_SCHOLAR_API_KEY.
+2. Prepare a Master Papers CSV 
+3. Update the 'titles' list with any additional papers to include.
+4. Update the 'technique_to_title' dictionary to map paper titles to the techniques they represent.
+5. Run the script, uncommenting the desired methods in the 'if __name__ == "__main__":' block.
+
+The script will save the processed data to JSON files at each step, and display the visualizations using matplotlib and the graph as a PNG.
+"""
+
+
 class SemanticScholarAPI:
     def __init__(self, api_key):
         self.api_key = api_key

From bcf2e49c5c75ed4e72744976011119174b5bb21d Mon Sep 17 00:00:00 2001
From: Sander Schulhoff <sanderschulhoff@gmail.com>
Date: Wed, 1 May 2024 11:05:45 -0400
Subject: [PATCH 18/19] feat: mains

---
 .../experiments/graph_dataset_citations.py     |  5 +++++
 .../experiments/graph_models.py                | 18 +++++++++++++-----
 2 files changed, 18 insertions(+), 5 deletions(-)

diff --git a/src/prompt_systematic_review/experiments/graph_dataset_citations.py b/src/prompt_systematic_review/experiments/graph_dataset_citations.py
index 00c2e1e..7e5f9b0 100644
--- a/src/prompt_systematic_review/experiments/graph_dataset_citations.py
+++ b/src/prompt_systematic_review/experiments/graph_dataset_citations.py
@@ -85,10 +85,15 @@ def graph_dataset_citations():
 
     output_dir = os.path.join(DataFolderPath, "experiments_output")
     os.makedirs(output_dir, exist_ok=True)
+    # output_file_path = os.path.join(output_dir, "graph_dataset_mentions_output.png")
     output_file_path = os.path.join(output_dir, "graph_dataset_mentions_output.png")
+
     plt.savefig(output_file_path)
 
 
 class Experiment:
     def run():
         graph_dataset_citations()
+
+if __name__ == "__main__":
+    graph_dataset_citations()
\ No newline at end of file
diff --git a/src/prompt_systematic_review/experiments/graph_models.py b/src/prompt_systematic_review/experiments/graph_models.py
index 05e6553..440e832 100644
--- a/src/prompt_systematic_review/experiments/graph_models.py
+++ b/src/prompt_systematic_review/experiments/graph_models.py
@@ -15,19 +15,27 @@ def graph_models(inputFile="model_citation_counts.csv"):
 
     plt.figure(figsize=(10, 6))
     plt.bar(top_20["model_name"], top_20["count"], color="#2E8991")
-    plt.xlabel("Model Name")
-    plt.ylabel("Count")
-    plt.title("Counts of Model Mentions in Dataset")
-    plt.xticks(rotation=45, ha="right")
+    plt.xlabel("Model Name", fontsize=20)
+    plt.ylabel("Count", fontsize=20)
+    plt.title("Counts of Model Mentions in Dataset", fontsize=30)
+
+    plt.xticks(rotation=45, ha="right", fontsize=15)  # Adjust fontsize as needed
+    plt.yticks(fontsize=15)  # Adjust fontsize as needed
+
     plt.tight_layout()
 
     output_dir = os.path.join(DataFolderPath, "experiments_output")
     os.makedirs(output_dir, exist_ok=True)  # Create directory if it doesn't exist
 
-    output_file_path = os.path.join(output_dir, "graph_models_output.png")
+    # output_file_path = os.path.join(output_dir, "graph_models_output.png")
+    output_file_path = os.path.join("graph_models_output.png")
+
     plt.savefig(output_file_path)
 
 
 class Experiment:
     def run():
         graph_models()
+
+if __name__ == "__main__":
+    graph_models()
\ No newline at end of file

From 7ee5b6f0b6315aa5979c138bdee5354ac2b5b656 Mon Sep 17 00:00:00 2001
From: Sander Schulhoff <sanderschulhoff@gmail.com>
Date: Sun, 12 May 2024 17:01:58 -0700
Subject: [PATCH 19/19] feat: working

---
 .../experiments/graph_internal_references.py           | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/prompt_systematic_review/experiments/graph_internal_references.py b/src/prompt_systematic_review/experiments/graph_internal_references.py
index a464bee..a8b5b84 100644
--- a/src/prompt_systematic_review/experiments/graph_internal_references.py
+++ b/src/prompt_systematic_review/experiments/graph_internal_references.py
@@ -45,7 +45,9 @@
 5. Run the script, uncommenting the desired methods in the 'if __name__ == "__main__":' block.
 
 The script will save the processed data to JSON files at each step, and display the visualizations using matplotlib and the graph as a PNG.
-"""
+
+
+May need to uncomment some stuff at the bottom for this to work."""
 
 
 class SemanticScholarAPI:
@@ -295,8 +297,8 @@ def visualize_graph(self, paper_references, technique_to_title):
                 display_title = technique_to_title.get(full_title, full_title)
                 wrapped_title = self.wrap_text(display_title, 10)
                 titles_above_threshold[paper_id] = wrapped_title
-
-        node_sizes = [((G.in_degree(node) * 8) + 35) for node in top_nodes]
+        
+        node_sizes = [((G.in_degree(node) * 80) + 50) for node in top_nodes]
 
         font_sizes = {node: G.in_degree(node) * 0.001 + 12 for node in top_nodes}
 
@@ -328,7 +330,7 @@ def visualize_graph(self, paper_references, technique_to_title):
             )
 
         plt.axis("off")
-        plt.show()
+        # plt.show()
         plt.savefig('network_graph.png', format='png', dpi=300)