A simplified algorithm for identifying abnormal changes in dynamic networks

Bouchaib Azamir, Driss Bennis and Bertrand Michel

Physica A: Statistical Mechanics and its Applications, 2022, vol. 607, issue C

Abstract: Topological data analysis has recently been applied to the study of dynamic networks. In this context, an algorithm was introduced and helps, among other things, to detect early warning signals of abnormal changes in the dynamic network under study. However, the complexity of this algorithm increases significantly once the database studied grows. In this paper, we propose a simplification of the algorithm without affecting its performance. We give various applications and simulations of the new algorithm on some weighted networks. The obtained results show clearly the efficiency of the introduced approach. Moreover, in some cases, the proposed algorithm makes it possible to highlight local information and sometimes early warning signals of local abnormal changes.

Keywords: Persistent homology; Closeness centrality in a network; Central subnetwork; Time series (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:607:y:2022:i:c:s0378437122007828

DOI: 10.1016/j.physa.2022.128224

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