Python package built to ease deep learning on graph, on top of existing DL frameworks.

Hands-On Graph Neural Networks Using Python, published by Packt

A curated list of datasets, publically available for machine learning research in the area of manufacturing

A curated collection of public industrial datasets.

A TensorFlow implementation of Relational Graph Attention Networks, paper: https://arxiv.org/abs/1904.05811

Revisiting, benchmarking, and refining Heterogeneous Graph Neural Networks.

Graph Neural Network Library for PyTorch

Gephi - The Open Graph Viz Platform

Awesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We…

My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entr…

[VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

KDD 2019: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

Data management framework for Python that provides functionality to describe, extract, validate, and transform tabular data

The Numenta Anomaly Benchmark

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning

Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021)