Fragmenting complex network based on non-backtracking matrix. Issue 3 (8th March 2021)

• Record Type: Journal Article
• Authors: Yuan, Rong-Hua; Tan, Fu-Rui; Chen, Wen-Zhou; Zhou, Ming-Yang
• Is Part Of: Journal of statistical mechanics. Issue 3(2021)
• Publication Date: 2021-03-08
• Subjects: influential node -- network percolation -- complex network; Statistical mechanics -- Periodicals; Mechanics -- Statistical methods -- Periodicals; 530.1305
• DOI: 10.1088/1742-5468/abe2a1
• Languages: English
• ISSNs: 1742-5468

Abstract

Abstract: Identifying the influential nodes to fragment a network is of significant importance in hindering the spread of epidemics and maximizing the influence of advertisements. Here, we address the problem by minimizing the largest eigenvalue of the non-backtracking matrices of networks, based on which a novel method is proposed to identify the optimal influential nodes. Interestingly, the proposed method could find some particular nodes to fragment complex networks. Experiments in real networks demonstrate the effectiveness of the proposed method.