graph-convolutional-networks

Graph Neural Network Library for PyTorch

links to conference publications in graph-based deep learning

StellarGraph - Machine Learning on Graphs

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources

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

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

[IEEE T-KDE 2026] Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).

A list of recent papers about Graph Neural Network methods applied in NLP areas.

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

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

This is the repository for the collection of Graph-based Deep Learning for Communication Networks.

A tensorflow implementation of Knowledge Graph Convolutional Networks