Nodes and layers PageRank centrality for multilayer networks

doi:10.1088/1674-1056/28/2/020501

Abstract In this paper, we propose a new centrality algorithm that can simultaneously rank the nodes and layers of multilayer networks, referred to as the MRFNL centrality. The centrality of nodes and layers are obtained by developing a novel iterative algorithm for computing a set of tensor equations. Under some conditions, the existence and uniqueness of this centrality were proven by applying the Brouwer fixed point theorem. Furthermore, the convergence of the proposed iterative algorithm was established. Finally, numerical experiments on a simple multilayer network and two real-world multilayer networks (i.e., Pierre Auger Collaboration and European Air Transportation Networks) are proposed to illustrate the effectiveness of the proposed algorithm and to compare it to other existing centrality measures.

Keywords: multilayer networks PageRank centrality random walks transition probability tensors

Received: 20 September 2018
Revised: 11 November 2018
Accepted manuscript online:

PACS:	05.40.Fb	(Random walks and Levy flights)
	89.75.Hc	(Networks and genealogical trees)
	89.75.-k	(Complex systems)

Fund: Project supported by the Postgraduate Research and Practice Innovation Program of Jiangsu Province, China (Grant No. AE91313/001/016), the National Natural Science Foundation of China (Grant No. 11701097), and the Natural Science Foundation of Jiangxi Province, China (Grant No. 20161BAB212055).

Corresponding Authors: Kun Zhang
E-mail: zhangkun@njust.edu.cn

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Lai-Shui Lv(吕来水), Kun Zhang(张琨), Ting Zhang(张婷), Meng-Yue Ma(麻孟越) Nodes and layers PageRank centrality for multilayer networks 2019 Chin. Phys. B 28 020501

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Nodes and layers PageRank centrality for multilayer networks

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