links to conference publications in graph-based deep learning

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

Repository for benchmarking graph neural networks

Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org

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

An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)

Strategies for Pre-training Graph Neural Networks

PyGCL: A PyTorch Library for Graph Contrastive Learning

Code for "Heterogeneous Graph Transformer" (WWW'20), which is based on pytorch_geometric

Recipe for a General, Powerful, Scalable Graph Transformer

Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)

ICLR 2020: Composition-Based Multi-Relational Graph Convolutional Networks

A tensorflow implementation of GraphGAN (Graph Representation Learning with Generative Adversarial Nets)

Code and resources on scalable and efficient Graph Neural Networks

[IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models

Code for KDD'20 "Generative Pre-Training of Graph Neural Networks"

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

A curated list for awesome self-supervised learning for graphs.

A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).

[GRL+ @ ICML 2020] PyTorch implementation for "Deep Graph Contrastive Representation Learning" (https://arxiv.org/abs/2006.04131v2)