graph-attention-network 话题在 GitHub 上没有填写简介，这里按 Star 数汇总最相关的热门仓库。
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!
Heterogeneous Graph Neural Network
A list of recent papers about Graph Neural Network methods applied in NLP areas.
The source codes for Fine-grained Fact Verification with Kernel Graph Attention Network.
异形图神经网络HAN.异构图注意力网络（HAN）与pytorch
GraphSAGE and GAT for link prediction.
Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction: SkeletonDiffusion, a latent diffusion model with attention graph architecture
The GitHub repository for the paper "Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations"
PyTorch implementation of Graph Attention Networks
IDPS-ESCAPE: Intrusion Detection and Prevention System - Enhanced Security through a Cooperative Anomaly Prediction Engine. ML-driven SOAR via a Risk-aware Anomaly Detection and Automated Response (RADAR) subsystem and a deep learning-based AD subsystem (SONAR), integrated with Wazuh, Flowintel, MISP, Suricata and SATRAP-DL.
Course project of SJTU CS3319: Foundations of Data Science, 2023 spring
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering
Heterogenous Graph Attention Transformer for high-resolution, distributed spatiotemporal flood prediction. Published at ACM SIGSPATIAL 2025.
Pytorch implementation of graph attention network
CryptoGAT: Are Time Series Models Effective for Cryptocurrency Forecasting?
GATor is a Graph Attention Network for object detection with relational reasoning
Modeling Extent-of-Texture Information for Ground Terrain Recognition
A deep learning library to rank protein complexes using graph neural networks
Detecting Hallucinations in Large Language Model Generations using Graph Structures
Source code accompanying the paper "Reducing Over-smoothing in Graph Neural Networks Using Relational Embeddings" published in DLG-AAAI’23