graph-representation-learning

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

links to conference publications in graph-based deep learning

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

Repository for benchmarking graph neural networks

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

Strategies for Pre-training Graph Neural Networks

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

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

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

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

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

Transformer for Graph Classification (Pytorch and Tensorflow)

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

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)

Efficient Graph Neural Networks - a curated list of papers and projects

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

A curated list for awesome graph representation learning resources.

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

Subgraph Neural Networks (NeurIPS 2020)

Source code for EvalNE, a Python library for evaluating Network Embedding methods.

Inductive relation prediction by subgraph reasoning, ICML'20

Variational Graph Recurrent Neural Networks - PyTorch

A curated list of awesome graph representation learning.

Code for the paper 'Learning TSP Requires Rethinking Generalization' (arXiv Pre-print)

Daily reading group on graphs at KEG

Transformers are Graph Neural Networks!

PyGCL: Graph Contrastive Learning Library for PyTorch

Graph Embedding via Frequent Subgraphs