Multi-Perspective Anomaly Detection on Bipartite Multi-Layer Social Interaction Networks

Autor(en): Maulana, Asep
Atzmueller, Martin 
Herausgeber: Koert, D.
Technical Univerity Darmstadt
Department of Computer Science
Hochschulstrasse 10
Darmstadt
Minor, M.
Stichwörter: Anomaly detection; Machine learning; Network layers; Anomaly detection; Complex network analysis; Interaction networks; Machine-learning; Many-objective optimizations; Multi-layer network; Multi-layers; Multi-perspective; Property; Social interactions; Complex networks
Erscheinungsdatum: 2022
Herausgeber: CEUR-WS
Journal: CEUR Workshop Proceedings
Volumen: 3457
Zusammenfassung: 
Anomaly detection is a prominent research direction in machine learning and complex network analysis. In this paper, we target a special type of complex networks, i. e., bipartite multi-layer networks. Here, we exploit the properties of such a complex network, i. e., the partitioning of the set of nodes into two groups, and its multi-layer characteristics. Our proposed approach includes many-objective optimization, correlation analysis and clustering – based on Eigenvector centrality – incorporated into a novel framework for identifying candidates for anomalous nodes from multiple perspectives, in a human-centered interpretable way. We exemplify the application of the proposed approach in a case study using a real-world dataset on socio-spatial interaction data. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Beschreibung: 
Cited by: 0; Conference name: Joint of 45th German Conference on Artificial Intelligence Workshops, Tutorials and Doctoral Consortium, KI-JP 2022; Conference date: 19 September 2022 through 20 September 2022; Conference code: 191764
ISSN: 1613-0073
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171254852&partnerID=40&md5=44bbe853a9e10451d1255af1e2ea3258

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