GAWD: Graph anomaly detection in weighted directed graph databases

Meng Chieh Lee, Hung T. Nguyen, DImitris Berberidis, Vincent S. Tseng, Leman Akoglu

研究成果: Conference contribution同行評審

8 引文 斯高帕斯(Scopus)

摘要

Given a set of node-labeled directed weighted graphs, how to find the most anomalous ones? How can we summarize the normal behavior in the database without losing information? We propose GAWD, for detecting anomalous graphs in directed weighted graph databases. The idea is to (1) iteratively identify the "best"substructure (i.e., subgraph or motif) that yields the largest compression when each of its occurrences is replaced by a super-node, and (2) score each graph by how much it compresses over iterations - - the more the compression, the lower the anomaly score. Different from existing work [1] on which we build, GAWD exhibits (i) a lossless graph encoding scheme, (ii) ability to handle numeric edge weights, (iii) interpretability by common patterns, and (iv) scalability with running time linear in input size. Experiments on four datasets injected with anomalies show that GAWD achieves significantly better results than state-of-the-art baselines.

原文English
主出版物標題Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
編輯Michele Coscia, Alfredo Cuzzocrea, Kai Shu
發行者Association for Computing Machinery, Inc
頁面143-150
頁數8
ISBN(電子)9781450391283
DOIs
出版狀態Published - 8 11月 2021
事件13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 - Virtual, Online, Netherlands
持續時間: 8 11月 2021 → …

出版系列

名字Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021

Conference

Conference13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
國家/地區Netherlands
城市Virtual, Online
期間8/11/21 → …

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