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)

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

KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019

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)

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).

GraphCON (ICML 2022)

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

Fake news detector based on the content and users associated with it using BERT and Graph Attention Networks (GAT).

Source code for paper "Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks"