Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.

[ICLR 2020; IPDPS 2019] Fast and accurate minibatch training for deep GNNs and large graphs (GraphSAINT: Graph Sampling Based Inductive Learning Method).

Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)

Multi-turn dialogue baselines written in PyTorch

This is a PyTorch implementation of the GeniePath model in <GeniePath: Graph Neural Networks with Adaptive Receptive Paths> (https://arxiv.org/abs/1802.00910)

PyTorch code for ICPR 2020 paper "DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting"

GNN方法和模型的Pytorch实现。Pytorch implementation of GNN.

Graph Neural Networks

Gradient gating (ICLR 2023)

PyTorch implementation of the Graph Attention Networks (GAT) based on the paper "Graph Attention Network" by Velickovic et al - https://arxiv.org/abs/1710.10903v3

[NLPCC 2020] Sentence Constituent-Aware Aspect-Category Sentiment Analysis with Graph Attention Networks

Fraud Detection using various GNN models

An Explainable Geometric-Weighted Graph Attention Network (xGW-GAT) for Identifying Functional Networks Associated with Gait Impairment

A Pytorch based implementation of classical GNNs.

Pytorch implementation of graph attention network

Protein design using graph attention networks (GATs)

Leveraging BERT GAT CRF to NER

Comparative Analysis of Graph Neural Networks for Node Regression on Wiki-Squirrel dataset (bachelor's Research Project)

Principles of various GNN algorithms and implementation of various GNN algorithms using DGL[ GNN各种算法原理以及实现(DGL)，初学者必备]

A TensorFlow 2 implementation of Graph Attention Networks (GAT)