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

Abstract

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).