graph-convolutional-networks

Graph Neural Network Library for PyTorch

links to conference publications in graph-based deep learning

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

StellarGraph - Machine Learning on Graphs

A distributed graph deep learning framework.

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

A curated list of graph-based fraud, anomaly, and outlier detection papers & resources

Graph Convolutional Networks for Text Classification. AAAI 2019

🟠 A study guide to learn about Graph Neural Networks (GNNs)

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

Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org

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

An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)

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

Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).

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.

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

Tutorial: Graph Neural Networks for Natural Language Processing at EMNLP 2019 and CODS-COMAD 2020

A Deep Graph-based Toolbox for Fraud Detection

OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research