A collection of important graph embedding, classification and representation learning papers with implementations.

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

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!

A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).

PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019

resources for graph convolutional networks （图卷积神经网络相关资源）

Keras implementation of the graph attention networks (GAT) by Veličković et al. (2017; https://arxiv.org/abs/1710.10903)

An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).

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).

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

Free hands-on course about Graph Neural Networks using PyTorch Geometric.

DeepInf: Social Influence Prediction with Deep Learning

This project is a scalable unified framework for deep graph clustering.

A Context-aware Visual Attention-based training pipeline for Object Detection from a Webpage screenshot!

ECML 2019: Graph Neural Networks for Multi-Label Classification

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

[ICDE'2023] When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks

[ICDE2023] A PyTorch implementation of Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics Framework (START).