#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...
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.
Official repository for survey paper "Deep Graph Anomaly Detection: A Survey and New Perspectives", including diverse types of resources for graph anomaly detection.
#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.
[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 for NeurIPS'24 paper "Generative Semi-supervised Graph Anomaly Detection"
#计算机科学#[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"
#计算机科学#Implementation of the paper Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation(WSDM22)
Source Code for Paper "DAGAD: Data Augmentation for Graph Anomaly Detection" ICDM 2022
Code for "Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts"
#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.
The source code of Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection (RAND), ICDM 2023.
[NeurIPS 2023 : GLFRONTIERS Workshop] GAD-EBM : Graph Anomaly Detection using Energy-Based Models
#计算机科学#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"