A Python Library for Graph Outlier Detection (Anomaly Detection)

Code for Deep Anomaly Detection on Attributed Networks (SDM2019)

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".

[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"

Implementation of the paper Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation(WSDM22)

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

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

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

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (CoLA), TNNLS-21

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

An official source code for paper "ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness", accepted by IEEE TNNLS.

Source code for DASFAA'24 paper "Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph"

The official PyTorch implementation of Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment (AAAI2023, to appear).

Code for ECMLPKDD23 paper "Graph-level Anomaly Detection via Hierarchical Memory Networks" (HimNet)

A Python Library for Graph Outlier Detection (Anomaly Detection)

Code Repository for Paper "HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks"