VN_Producer_Influencer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Run All"
      ],
      "metadata": {
        "id": "dqPTJfieVGuv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import and Load"
      ],
      "metadata": {
        "id": "tYE7az_TVI_a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import library yang dibutuhkan"
      ],
      "metadata": {
        "id": "4enSTSAyubGk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HfarWBYwUH4j"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import networkx as nx\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rcParams\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mengubah Style"
      ],
      "metadata": {
        "id": "Tlke4cg-udxv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rcParams['figure.figsize'] = 15, 8\n",
        "mpl.style.use(['ggplot'])"
      ],
      "metadata": {
        "id": "QTBl5kAJjn9c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load Dataset"
      ],
      "metadata": {
        "id": "DsO6hkpxufpk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_producers_relation = pd.read_excel('https://drive.google.com/uc?id=16-5ODNyWU3hvnJoZUjdLrEeZWTQhgxO9')"
      ],
      "metadata": {
        "id": "aLRoXtPiUwrc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lihat Dataset"
      ],
      "metadata": {
        "id": "1G53BXPeujBB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_producers_relation.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KTsL462HVA1S",
        "outputId": "eccccaff-eac2-4efd-ded7-a18cc83951ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id    pid relation                  p1_name         p2_name\n",
              "0  p1   p266      par  Yamikumo-Communications        CD Bros.\n",
              "1  p2   p266      par                 Sekilala        CD Bros.\n",
              "2  p3   p102      par            G-Collections        JAST USA\n",
              "3  p3   p266      par            G-Collections        CD Bros.\n",
              "4  p4  p5633      ipa                      age  ACID Co., Ltd."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8eee154-d9f2-475f-a79d-19ea7410098b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>pid</th>\n",
              "      <th>relation</th>\n",
              "      <th>p1_name</th>\n",
              "      <th>p2_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>p1</td>\n",
              "      <td>p266</td>\n",
              "      <td>par</td>\n",
              "      <td>Yamikumo-Communications</td>\n",
              "      <td>CD Bros.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>p2</td>\n",
              "      <td>p266</td>\n",
              "      <td>par</td>\n",
              "      <td>Sekilala</td>\n",
              "      <td>CD Bros.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>p3</td>\n",
              "      <td>p102</td>\n",
              "      <td>par</td>\n",
              "      <td>G-Collections</td>\n",
              "      <td>JAST USA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>p3</td>\n",
              "      <td>p266</td>\n",
              "      <td>par</td>\n",
              "      <td>G-Collections</td>\n",
              "      <td>CD Bros.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>p4</td>\n",
              "      <td>p5633</td>\n",
              "      <td>ipa</td>\n",
              "      <td>age</td>\n",
              "      <td>ACID Co., Ltd.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8eee154-d9f2-475f-a79d-19ea7410098b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b8eee154-d9f2-475f-a79d-19ea7410098b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b8eee154-d9f2-475f-a79d-19ea7410098b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ukuran Dataset"
      ],
      "metadata": {
        "id": "KtBoY76SumUE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_producers_relation.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDAkdxVsVMN0",
        "outputId": "d17afe6f-df95-49e1-f5ce-d045b4b103cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5086, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Info Dataset"
      ],
      "metadata": {
        "id": "nryWLg6buoXl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_producers_relation.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fh7ESFF2VPEq",
        "outputId": "9b987295-a99d-4814-a2c3-fa84d372f48a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5086 entries, 0 to 5085\n",
            "Data columns (total 5 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   id        5086 non-null   object\n",
            " 1   pid       5086 non-null   object\n",
            " 2   relation  5086 non-null   object\n",
            " 3   p1_name   5086 non-null   object\n",
            " 4   p2_name   5086 non-null   object\n",
            "dtypes: object(5)\n",
            "memory usage: 198.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Graf"
      ],
      "metadata": {
        "id": "idKYLAsvaK6T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Membuat Graf"
      ],
      "metadata": {
        "id": "fgnCOSzmuUgu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p_graph = nx.Graph()\n",
        "\n",
        "for i in range(len(df_producers_relation)):\n",
        "  p_graph.add_edge(df_producers_relation['p1_name'][i], df_producers_relation['p2_name'][i])"
      ],
      "metadata": {
        "id": "jICiNk3giDNp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualisasi Graf. Waktu Run sekitar 40 detik"
      ],
      "metadata": {
        "id": "vjs8cFyRnEbk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nx.draw_networkx(p_graph, node_size=20, width=1, alpha=0.8, with_labels=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "9-c4GKQNmJ-c",
        "outputId": "a54644ec-b2c5-487f-b4c9-3e81b15b99ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perhitungan Centrality"
      ],
      "metadata": {
        "id": "QeY52fnUsIJW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Degree Centrality"
      ],
      "metadata": {
        "id": "yffxC_ScofdV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menggunakan degree_centrality()"
      ],
      "metadata": {
        "id": "OL2hotFrsLnZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nilai_degree = nx.degree_centrality(p_graph)\n",
        "nilai_degree = dict(sorted(nilai_degree.items(), key=lambda item: item[1],reverse=True))\n",
        "# print(nilai_centrality)"
      ],
      "metadata": {
        "id": "GZHFYho5l0Ji"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hasil menjadi Dataframe"
      ],
      "metadata": {
        "id": "APcpzCrksPCS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list_influence = list(nilai_degree.keys())\n",
        "list_nilai = list(nilai_degree.values())\n",
        "\n",
        "df_degree = pd.DataFrame({'Top by Degree' : list_influence, 'Degree Centrality' : list_nilai})\n",
        "df_degree.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "0bG0pdCGpM15",
        "outputId": "f2b543b1-59e3-4247-88d5-a5d0cd50443b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Top by Degree  Degree Centrality\n",
              "0              VISUAL ARTS           0.021821\n",
              "1                 WillPlus           0.013297\n",
              "2                   Nexton           0.009206\n",
              "3                  Hobibox           0.007501\n",
              "4                     Giga           0.006478\n",
              "5  Space Project Co., Ltd.           0.006478\n",
              "6                 CD Bros.           0.006137\n",
              "7     Interheart Co., Ltd.           0.005796\n",
              "8        PRODUCTION PENCIL           0.005114\n",
              "9                 MasterUp           0.005114"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1ba2cff7-32c7-4746-be01-500c7ed5d8a2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Top by Degree</th>\n",
              "      <th>Degree Centrality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>VISUAL ARTS</td>\n",
              "      <td>0.021821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>WillPlus</td>\n",
              "      <td>0.013297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Nexton</td>\n",
              "      <td>0.009206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hobibox</td>\n",
              "      <td>0.007501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Giga</td>\n",
              "      <td>0.006478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Space Project Co., Ltd.</td>\n",
              "      <td>0.006478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>CD Bros.</td>\n",
              "      <td>0.006137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Interheart Co., Ltd.</td>\n",
              "      <td>0.005796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>PRODUCTION PENCIL</td>\n",
              "      <td>0.005114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>MasterUp</td>\n",
              "      <td>0.005114</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ba2cff7-32c7-4746-be01-500c7ed5d8a2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1ba2cff7-32c7-4746-be01-500c7ed5d8a2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1ba2cff7-32c7-4746-be01-500c7ed5d8a2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Betweenness Centrality"
      ],
      "metadata": {
        "id": "hE3uZZwtpBsq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menggunakan betweenness_centrality(). Perkiraaan 11 detik"
      ],
      "metadata": {
        "id": "3XnN3EuqpBss"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nilai_betweenness = nx.betweenness_centrality(p_graph)\n",
        "nilai_betweenness = dict(sorted(nilai_betweenness.items(), key=lambda item: item[1],reverse=True))\n",
        "# print(nilai_centrality)"
      ],
      "metadata": {
        "id": "ZrydZcNqpBsu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hasil menjadi Dataframe"
      ],
      "metadata": {
        "id": "uVvGFVVppBsv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list_influence = list(nilai_betweenness.keys())\n",
        "list_nilai = list(nilai_betweenness.values())\n",
        "\n",
        "df_betweenness = pd.DataFrame({'Top by Betweenness' : list_influence, 'Betwenness Centrality' : list_nilai})\n",
        "df_betweenness.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "a32b6074-be28-4a30-8e42-7ddb74211032",
        "id": "UKOI-uFDpBsv"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Top by Betweenness  Betwenness Centrality\n",
              "0            VISUAL ARTS               0.028919\n",
              "1                Hobibox               0.026658\n",
              "2                 Nexton               0.021444\n",
              "3                   IDES               0.016557\n",
              "4          F&C Co., Ltd.               0.016385\n",
              "5  STONE HEADS Co., Ltd.               0.015169\n",
              "6                 Kirara               0.014513\n",
              "7              CRAFTWORK               0.013393\n",
              "8                    PIL               0.013320\n",
              "9               Yumemiru               0.013249"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-70c00fb4-daea-45c7-8a70-14cbfa8f3b40\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Top by Betweenness</th>\n",
              "      <th>Betwenness Centrality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>VISUAL ARTS</td>\n",
              "      <td>0.028919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hobibox</td>\n",
              "      <td>0.026658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Nexton</td>\n",
              "      <td>0.021444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IDES</td>\n",
              "      <td>0.016557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F&amp;C Co., Ltd.</td>\n",
              "      <td>0.016385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>STONE HEADS Co., Ltd.</td>\n",
              "      <td>0.015169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Kirara</td>\n",
              "      <td>0.014513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>CRAFTWORK</td>\n",
              "      <td>0.013393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>PIL</td>\n",
              "      <td>0.013320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Yumemiru</td>\n",
              "      <td>0.013249</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70c00fb4-daea-45c7-8a70-14cbfa8f3b40')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-70c00fb4-daea-45c7-8a70-14cbfa8f3b40 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-70c00fb4-daea-45c7-8a70-14cbfa8f3b40');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Eigenvector Centrality"
      ],
      "metadata": {
        "id": "QueO8RMipCku"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menggunakan eigenvector_centrality()"
      ],
      "metadata": {
        "id": "W-8II1PgpCku"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nilai_eigenvector = nx.eigenvector_centrality(p_graph)\n",
        "nilai_eigenvector = dict(sorted(nilai_eigenvector.items(), key=lambda item: item[1],reverse=True))\n",
        "# print(nilai_centrality)"
      ],
      "metadata": {
        "id": "IykBTazmpCkv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hasil menjadi Dataframe"
      ],
      "metadata": {
        "id": "T1zFk5HGpCkw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list_influence = list(nilai_eigenvector.keys())\n",
        "list_nilai = list(nilai_eigenvector.values())\n",
        "\n",
        "df_eigenvector = pd.DataFrame({'Top by Eigenvector' : list_influence, 'Eigenvector Centrality' : list_nilai})\n",
        "df_eigenvector.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "327c393f-a8c1-4309-8503-993a1b20c785",
        "id": "oP4kaAIVpCkw"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Top by Eigenvector  Eigenvector Centrality\n",
              "0              VISUAL ARTS                0.700779\n",
              "1       Visual Art's Motto                0.111279\n",
              "2                     ZERO                0.111079\n",
              "3                PROTOTYPE                0.101686\n",
              "4  Softhouse Bonbee Bonbon                0.100301\n",
              "5     Visual Art's MottoSP                0.100115\n",
              "6              Studio Ring                0.100091\n",
              "7                   Miyabi                0.100091\n",
              "8                  Bonbee!                0.098762\n",
              "9                CRAFTWORK                0.089128"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0a95178b-a088-485f-9f78-b3d207f58959\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Top by Eigenvector</th>\n",
              "      <th>Eigenvector Centrality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>VISUAL ARTS</td>\n",
              "      <td>0.700779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Visual Art's Motto</td>\n",
              "      <td>0.111279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ZERO</td>\n",
              "      <td>0.111079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>PROTOTYPE</td>\n",
              "      <td>0.101686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Softhouse Bonbee Bonbon</td>\n",
              "      <td>0.100301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Visual Art's MottoSP</td>\n",
              "      <td>0.100115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Studio Ring</td>\n",
              "      <td>0.100091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Miyabi</td>\n",
              "      <td>0.100091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Bonbee!</td>\n",
              "      <td>0.098762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>CRAFTWORK</td>\n",
              "      <td>0.089128</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a95178b-a088-485f-9f78-b3d207f58959')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0a95178b-a088-485f-9f78-b3d207f58959 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0a95178b-a088-485f-9f78-b3d207f58959');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Influential Akhir"
      ],
      "metadata": {
        "id": "pEraEDmRrGaG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menggabungkan ketiga hasil centrality"
      ],
      "metadata": {
        "id": "BfOuhgF42u9j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_influential = pd.concat([df_degree.head(10), df_betweenness.head(10), df_eigenvector.head(10)], axis=1)\n",
        "df_influential"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "UHI8-Ygnrbrj",
        "outputId": "e76204b8-be8e-433a-cb90-8e89a77ebf99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Top by Degree  Degree Centrality     Top by Betweenness  \\\n",
              "0              VISUAL ARTS           0.021821            VISUAL ARTS   \n",
              "1                 WillPlus           0.013297                Hobibox   \n",
              "2                   Nexton           0.009206                 Nexton   \n",
              "3                  Hobibox           0.007501                   IDES   \n",
              "4                     Giga           0.006478          F&C Co., Ltd.   \n",
              "5  Space Project Co., Ltd.           0.006478  STONE HEADS Co., Ltd.   \n",
              "6                 CD Bros.           0.006137                 Kirara   \n",
              "7     Interheart Co., Ltd.           0.005796              CRAFTWORK   \n",
              "8        PRODUCTION PENCIL           0.005114                    PIL   \n",
              "9                 MasterUp           0.005114               Yumemiru   \n",
              "\n",
              "   Betwenness Centrality       Top by Eigenvector  Eigenvector Centrality  \n",
              "0               0.028919              VISUAL ARTS                0.700779  \n",
              "1               0.026658       Visual Art's Motto                0.111279  \n",
              "2               0.021444                     ZERO                0.111079  \n",
              "3               0.016557                PROTOTYPE                0.101686  \n",
              "4               0.016385  Softhouse Bonbee Bonbon                0.100301  \n",
              "5               0.015169     Visual Art's MottoSP                0.100115  \n",
              "6               0.014513              Studio Ring                0.100091  \n",
              "7               0.013393                   Miyabi                0.100091  \n",
              "8               0.013320                  Bonbee!                0.098762  \n",
              "9               0.013249                CRAFTWORK                0.089128  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-97f85f9e-7c3e-4da7-b745-903256fa4c0e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Top by Degree</th>\n",
              "      <th>Degree Centrality</th>\n",
              "      <th>Top by Betweenness</th>\n",
              "      <th>Betwenness Centrality</th>\n",
              "      <th>Top by Eigenvector</th>\n",
              "      <th>Eigenvector Centrality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>VISUAL ARTS</td>\n",
              "      <td>0.021821</td>\n",
              "      <td>VISUAL ARTS</td>\n",
              "      <td>0.028919</td>\n",
              "      <td>VISUAL ARTS</td>\n",
              "      <td>0.700779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>WillPlus</td>\n",
              "      <td>0.013297</td>\n",
              "      <td>Hobibox</td>\n",
              "      <td>0.026658</td>\n",
              "      <td>Visual Art's Motto</td>\n",
              "      <td>0.111279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Nexton</td>\n",
              "      <td>0.009206</td>\n",
              "      <td>Nexton</td>\n",
              "      <td>0.021444</td>\n",
              "      <td>ZERO</td>\n",
              "      <td>0.111079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hobibox</td>\n",
              "      <td>0.007501</td>\n",
              "      <td>IDES</td>\n",
              "      <td>0.016557</td>\n",
              "      <td>PROTOTYPE</td>\n",
              "      <td>0.101686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Giga</td>\n",
              "      <td>0.006478</td>\n",
              "      <td>F&amp;C Co., Ltd.</td>\n",
              "      <td>0.016385</td>\n",
              "      <td>Softhouse Bonbee Bonbon</td>\n",
              "      <td>0.100301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Space Project Co., Ltd.</td>\n",
              "      <td>0.006478</td>\n",
              "      <td>STONE HEADS Co., Ltd.</td>\n",
              "      <td>0.015169</td>\n",
              "      <td>Visual Art's MottoSP</td>\n",
              "      <td>0.100115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>CD Bros.</td>\n",
              "      <td>0.006137</td>\n",
              "      <td>Kirara</td>\n",
              "      <td>0.014513</td>\n",
              "      <td>Studio Ring</td>\n",
              "      <td>0.100091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Interheart Co., Ltd.</td>\n",
              "      <td>0.005796</td>\n",
              "      <td>CRAFTWORK</td>\n",
              "      <td>0.013393</td>\n",
              "      <td>Miyabi</td>\n",
              "      <td>0.100091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>PRODUCTION PENCIL</td>\n",
              "      <td>0.005114</td>\n",
              "      <td>PIL</td>\n",
              "      <td>0.013320</td>\n",
              "      <td>Bonbee!</td>\n",
              "      <td>0.098762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>MasterUp</td>\n",
              "      <td>0.005114</td>\n",
              "      <td>Yumemiru</td>\n",
              "      <td>0.013249</td>\n",
              "      <td>CRAFTWORK</td>\n",
              "      <td>0.089128</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97f85f9e-7c3e-4da7-b745-903256fa4c0e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-97f85f9e-7c3e-4da7-b745-903256fa4c0e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-97f85f9e-7c3e-4da7-b745-903256fa4c0e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Export ke Excel"
      ],
      "metadata": {
        "id": "Fc3tM_8Z2x41"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_influential.to_excel(r'vn-producer-influencer.xlsx', index=False, header=True)"
      ],
      "metadata": {
        "id": "AA4nSD9F2zHS"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Statistik"
      ],
      "metadata": {
        "id": "Mo-LOkOQwnm-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Banyaknya Nodes = \", p_graph.number_of_nodes())\n",
        "print(\"Banyaknya Edges = \", p_graph.number_of_edges())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-2_qxXGwqHE",
        "outputId": "2d6616e4-ad40-4e68-89c0-26d83cfbe21f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Banyaknya Nodes =  2934\n",
            "Banyaknya Edges =  2543\n"
          ]
        }
      ]
    }
  ]
}