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

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

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 (TNNLS 2023)

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).

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

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

Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022

Official Pytorch code for Structure-Aware Transformer.

Inductive relation prediction by subgraph reasoning, ICML'20

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

A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

Segment Anything Model for large-scale, vectorized road network extraction from aerial imagery. CVPRW 2024