Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).

A pytorch adversarial library for attack and defense methods on images and graphs

A scikit-learn compatible library for graph kernels

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

Implementation of the paper "Adversarial Attacks on Neural Networks for Graph Data".

Python toolbox to evaluate graph vulnerability and robustness (CIKM 2021)

Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".

Python Implementation for Random Walk with Restart (RWR)

A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).

Implementation of paper "Self-supervised Learning on Graphs:Deep Insights and New Directions"

An alternative implementation of Recursive Feature and Role Extraction (KDD11 & KDD12)

Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"

The source code of a community detection method in dynamic networks for paper "IncNSA: Detecting communities incrementally from time-evolving networks based on node similarity".

[ICML'24] BAT: 🚀 Boost Class-imbalanced Node Classification with <10 lines of Code | 从拓扑视角出发10行代码改善类别不平衡节点分类

Pytorch implementation of gnn meta attack (mettack). Paper title: Adversarial Attacks on Graph Neural Networks via Meta Learning.

"Explainable classification of brain networks via contrast subgraphs" - T. Lanciano, F. Bonchi, A. Gionis

Implementation of paper "Transferring Robustness for Graph Neural Network Against Poisoning Attacks".

Code for "Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation"

k-hop Graph Neural Networks

Code for paper "AutoAudit: Mining Accounting and Time-Evolving Graphs" (Big Data 2020)