 Co-Ranking for nodes, layers and timestamps in multilayer temporal networksTing Zhang, Kun Zhang, Laishui Lv and Dalal Bardou Chaos, Solitons & Fractals, 2019, vol. 125, issue C, 88-96 Abstract: Understanding the structure of multilayer temporal networks requires the evaluation of nodes importance, the relationship between them and the timestamps simultaneously. In this paper,we propose a parameters-free centrality algorithm referred to as Co-Rank. The proposed algorithm uses a sixth-order tensor to describe the multilayer temporal network which considers the inter-layer connections between the adjacent timestamps across different layers. After describing the multilayer temporal network, the next step is to build and solve a set of tensor equations following the mutual relationships to get the centrality. The existence of the centrality metric is formally proven, and the convergence of the Co-Rank is also shown so that it can be effectively applied for the ranking. The results of experiments on synthetic and real-world networks show the effectiveness of our proposed algorithm. Keywords: Multilayer temporal network; Centrality; Random walk; Transition probability tensors (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:125:y:2019:i:c:p:88-96 DOI: 10.1016/j.chaos.2019.05.021 Access Statistics for this article Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros More articles in Chaos, Solitons & Fractals from Elsevier |
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