ReadGraph: Relational Evolution Enhanced Anomaly Detection in Dynamic Heterogeneous Graph
Abnormal behavior detection is crucial in many fields, such as social networks, financial transactions, and cybersecurity. However, it poses significant challenges due to the intricate structural evolution of heterogeneous graphs. To address this issue, we propose a novel method called <u>R</u>elational <u>E</u>volution enhanced <u>A</u>nomaly <u>D</u>etection in dynamic heterogeneous <u>Graph</u> (ReadGraph ). ReadGraph focuses on tracing relation-based dynamic structural evolution to comprehensively capture features related to abnormal behaviors (edges) across different types of nodes. We conduct extensive experiments to evaluate ReadGraph against advanced competitors. It demonstrates that ReadGraph is 13.69% more effective than other methods on average.
Authors
- Xuequn Shang (ORCID: https://orcid.org/0000-0002-7249-8210)
- Xiaolin Han (ORCID: https://orcid.org/0000-0002-4347-1692)
- Chenhao Ma (ORCID: https://orcid.org/0000-0002-3243-8512)
- Xiurui Hu
Institutions
- Northwestern Polytechnical University (CN)
- Chinese University of Hong Kong, Shenzhen (CN)
Publication Details
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1145/3701716.3715509
- Citations
- 1
- Primary Topic
- Complex Network Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.32
Funders
- Ant Group
- National Natural Science Foundation of China
- Chinese University of Hong Kong
- Shenzhen Research Institute of Big Data
- Shenzhen Research Institute, City University of Hong Kong
- Fundamental Research Funds for the Central Universities