A Python Library for Graph Outlier Detection (Anomaly Detection)
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Updated
Jul 5, 2022 - Python
A Python Library for Graph Outlier Detection (Anomaly Detection)
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)
Source code for DASFAA'24 paper "Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA Graph"
Code Repository for Paper "HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks"
A repository for resources of deep learning-based graph anomaly detection.
[NeurIPS 2023 : GLFRONTIERS Workshop] GAD-EBM : Graph Anomaly Detection using Energy-Based Models
An official source code for paper "ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awareness", accepted by IEEE TNNLS.
The source code of Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection (RAND), ICDM 2023.
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (CoLA), TNNLS-21
An official source code for paper "Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning", accepted by ACM MM 2023.
Source Code for Paper "DAGAD: Data Augmentation for Graph Anomaly Detection" ICDM 2022
This code is for paper "Generative Semi-supervised Graph Anomaly Detection"
Implementation of the paper Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation(WSDM22)
[WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction
Official implementation of NeurIPS'23 paper "Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection"
[TKDE 2021] A PyTorch implementation of "Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection".
An official source code for paper "Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View", accepted by AAAI 2023.
A collection of papers for graph anomaly detection, and published algorithms and datasets.
Code for Deep Anomaly Detection on Attributed Networks (SDM2019)
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