CFG based program similarity using Graph Neural Networks

A Survey of Learning from Graphs with Heterophily

Listing the research works related to risk control based on GNN and its interpretability. 1. we can learn the application of GNN in risk control (including fraud detection). 2. For possible prediction, we can use the interpretability of GNN to explaine how can we get such results.

A collection of GNN projects

Awesome GNN Learning For beginners

An implementation from scratch of Graph Convolutional Networks (GCN) using Numpy

with GUG, Let's explore the Graph Neural Network!

code & report files for Project of EE394V SPR 2021

The repository is a collection of Jupyter notebooks showcasing various projects related to graph neural networks (GNNs). Each notebook provides a detailed explanation of the project and its implementation, making it easy for users to understand and replicate the results.

links to conference publications in graph-based deep learning

Originally the Intersectional Graph Project, SURRF (The Semantic Ubiquitous Relations Research Framework) is an AI-assisted, graph-based research engine, built with the perplexity.ai API and NextJS. SURRF enables researchers to navigate the web while creating knowledge graphs that users can save, organize, compile, and share.

SMILES converted into Graphs that contains atomic information, bonding informatics. Graphs considered as input for the NN to Predict Melting Pont of Liquid Crystals (LCs)

In this project I explore an potential approach to estimate a human’s intention in a dyadic collaborative manipulation task by learning to predict the intended future trajectory of the co-manipulated object via the latent graph representation of the system.

Implement GNN models (GAT, GCN)

Literature indexing using Graph Neural Networks and label-guided text embeddings.

first commit

This repository is a brief tutorial about how Graph convolutional networks and message passing networks work with example code demonstration using pytorch and torch_geometric

This repository shows a use case of Graph ML for casinos in marketing: Market Segmentation . Skills: Azure SDK, azure datalake, node2vec, graphml, geometric pytorch, gnn

Official PyTorch Implementation of paper 'Edge-Based Graph Neural Networks for Cell-Graph Modeling and Prediction'