PytorchGAT

Code for "Training-free Graph Neural Networks and the Power of Labels as Features" (TMLR 2024)

My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!

CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)

An Industrial Graph Neural Network Framework

An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)

Code and resources on scalable and efficient Graph Neural Networks (TNNLS 2023)

A PyTorch Graph Neural Network Library

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.

1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.