graph-anomaly-detection

#计算机科学#A Python Library for Graph Outlier Detection (Anomaly Detection)

ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.

#计算机科学#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 al...

Official repository for 2025 TKDE survey paper "Deep Graph Anomaly Detection: A Survey and New Perspectives", including diverse types of resources for graph anomaly detection

Code for Deep Anomaly Detection on Attributed Networks (SDM2019)

#计算机科学#A collection of papers for graph anomaly detection, and published algorithms and datasets.

#计算机科学#An official source code for paper "Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View", accepted by AAAI 2023.

Official implementation for NeurIPS'24 paper "Generative Semi-supervised Graph Anomaly Detection"

[CIKM 2021] A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning".

#计算机科学#[WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction

[TKDE 2021] A PyTorch implementation of "Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection".

Official implementation of NeurIPS'23 paper "Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection"

#计算机科学#Implementation of the paper Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation(WSDM22)

Official PyTorch implementation of KDD2025 paper "AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection"

Official Code for IJCAI25 paper "Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts"

Source Code for Paper "DAGAD: Data Augmentation for Graph Anomaly Detection" ICDM 2022

#计算机科学#An official source code for paper "Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning", accepted by ACM MM 2023.

Official PyTorch implementation of ''Semi-supervised Graph Anomaly Detection via Robust Homophily Learning'' https://arxiv.org/abs/2506.15448

Official PyTorch implementation of ICLR'25 paper "Open-Set Graph Anomaly Detection via Normal Structure Regularisation"

The source code of Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection (RAND), ICDM 2023.