A Survey of Learning from Graphs with Heterophily

Code and Content for Manning Publication on Graph Neural Networks

CFG based program similarity using Graph Neural Networks

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

Awesome GNN Learning For beginners

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

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.

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

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.

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.

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

Implement GNN models (GAT, GCN)

TeloMesh is a UX journey intelligence engine for product teams that maps user flows, identifies friction points, and prioritizes fixes using graph theory. It leverages analytics from Mixpanel, Amplitude, and GA4 to surface the most impactful improvements.

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

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

A collection of GNN projects

links to conference publications in graph-based deep learning

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.

《深入浅出图神经网络：GNN原理解析》配套代码

Must-read papers on graph neural networks (GNN)