@inproceedings{79253c0751f54e0fb30eef86fbc8fab0,
title = "ReadGraph: Relational Evolution Enhanced Anomaly Detection in Dynamic Heterogeneous Graph",
abstract = "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 Relational Evolution enhanced Anomaly Detection in dynamic heterogeneous Graph (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.",
keywords = "Anomaly detection, Dynamic heterogeneous graph",
author = "Xiaolin Han and Xiurui Hu and Chenhao Ma and Xuequn Shang",
note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.; 34th ACM Web Conference, WWW Companion 2025 ; Conference date: 28-04-2025 Through 02-05-2025",
year = "2025",
month = may,
day = "23",
doi = "10.1145/3701716.3715509",
language = "英语",
series = "WWW Companion 2025 - Companion Proceedings of the ACM Web Conference 2025",
publisher = "Association for Computing Machinery, Inc",
pages = "1009--1013",
booktitle = "WWW Companion 2025 - Companion Proceedings of the ACM Web Conference 2025",
}