A library for graph deep learning research

《深入浅出图神经网络：GNN原理解析》配套代码

Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

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

Heterogeneous Graph Neural Network

A list of recent papers about Graph Neural Network methods applied in NLP areas.

Neural Graph Collaborative Filtering, SIGIR2019

Recipe for a General, Powerful, Scalable Graph Transformer

A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)

Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).

A repository of pretty cool datasets that I collected for network science and machine learning research.

[NeurIPS 2020] "Graph Contrastive Learning with Augmentations" by Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

图神经网络整理

This is the repository for the collection of Graph-based Deep Learning for Communication Networks.

Deep and conventional community detection related papers, implementations, datasets, and tools.

CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).

Representation-Learning-on-Heterogeneous-Graph

Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).

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

A curated list of Hypergraph Learning, Hypergraph Theory, Hypergraph Dataset and Hypergraph Tool.

Representation learning on dynamic graphs using self-attention networks

GFlowNet library specialized for graph & molecular data

Implementation of ICCV19 Paper "Learning Two-View Correspondences and Geometry Using Order-Aware Network"

Awesome Resources for Advanced Computer Vision Topics

A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.

All graph/GNN papers accepted at the International Conference on Machine Learning (ICML) 2024.

Learning Intents behind Interactions with Knowledge Graph for Recommendation, WWW2021

[ICLR 2023] One Transformer Can Understand Both 2D & 3D Molecular Data (official implementation)

A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).