Nodes and layers PageRank centrality for multilayer networks*

• 来源期刊: 中国物理B（英文版）
• 关键词: multilayer networks PageRank centrality random walks transition probability tensors
• 年，卷（期）: 2019,（2）
• 页码范围: 125-132
• 页数: 8页
• 语种: 英文
• DOI: 10.1088/1674-1056/28/2/020501

摘要

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.