Eigenvector-based centralities for multilayer temporal networks under the framework of tensor computation
作者：
Highlights：
• Using sixth-order tensor to represent multilayer temporal networks.
• Cosine similarity is proposed to measure interactions between different layers.
• Multilayer temporal eigenvector and PageRank centralities are proposed.
• The convergence of the proposed iterative algorithms are established.
• The proposed two centrality measures are applied to three networks.
摘要
•Using sixth-order tensor to represent multilayer temporal networks.•Cosine similarity is proposed to measure interactions between different layers.•Multilayer temporal eigenvector and PageRank centralities are proposed.•The convergence of the proposed iterative algorithms are established.•The proposed two centrality measures are applied to three networks.
论文关键词：Multilayer temporal networks,Eigenvector centrality,PageRank centrality,Inter-layer similarity,Key nodes,Sixth-order tensor
论文评审过程：Received 16 June 2020, Revised 20 June 2021, Accepted 22 June 2021, Available online 1 July 2021, Version of Record 6 July 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115471
