XiaoxiaoMa-MQ/Awesome-Deep-Graph-Anomaly-Detection
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 also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contributors and boost further research in this area.
This resource helps researchers and practitioners find methods and datasets to detect unusual patterns or behaviors in complex network data. It provides a curated list of research papers, algorithms, and benchmark datasets, enabling users to identify anomalies like fraudulent transactions, system intrusions, or unusual activities within social, co-authorship, or transportation networks. It's intended for data scientists, machine learning engineers, and domain experts working with graph-structured data.
384 stars. No commits in the last 6 months.
Use this if you need to identify rare or suspicious nodes, edges, or subgraphs within interconnected data like social networks, financial transaction graphs, or citation networks.
Not ideal if you are looking for a ready-to-use software tool or a step-by-step tutorial for implementing anomaly detection, as this is primarily a curated research collection.
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Last pushed
Jul 10, 2023
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