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

CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)

A curated list of data mining papers about fraud detection.

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

A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).

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

A scikit-learn compatible library for graph kernels

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

Hierarchical Graph Pooling with Structure Learning

A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

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

Topological Graph Neural Networks (ICLR 2022)

Official Code Repository for the paper "Accurate Learning of Graph Representations with Graph Multiset Pooling" (ICLR 2021)

AAAI 2020 - ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations

A convolutional neural network for graph classification in PyTorch

A PyTorch implementation of DGCNN based on AAAI 2018 paper "An End-to-End Deep Learning Architecture for Graph Classification"

Official implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022).

A Persistent Weisfeiler–Lehman Procedure for Graph Classification

The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)

Official Code Repository for the paper "Edge Representation Learning with Hypergraphs" (NeurIPS 2021)