Critical node detection problem for complex network in undirected weighted networks

• Published in: Physica A Vol. 538; p. 122862
• Main Authors: Chen, Wei, Jiang, Manrui, Jiang, Cheng, Zhang, Jun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.01.2020
• Subjects: Critical nodes problem; Greedy algorithm; Mixed-integer quadratic programming; Network fragmentation; Weighted network; Greedy algorithm; Critical nodes problem; Network fragmentation; Mixed-integer quadratic programming; Weighted network
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2019.122862

Detection of critical nodes in complex networks has recently received extensive attention. Currently, studies of the critical nodes problem (CNP) mainly focus on two problem types: “critical nodes problem/positive” (CNP-Pos) and “critical nodes problem/negative” (CNP-Neg). However, to the best of our knowledge, few studies have been conducted on CNP-Neg for weighed networks. In this paper, we investigate CNP-Neg in undirected weighted networks. We first propose a novel metric DFW to evaluate network fragmentation. Then, we formulate a new nonconvex mixed-integer quadratic programming model, named MIQPM, that aims to simultaneously minimize pairwise connectivity and maximize the weights between the nodes. After that, a general greedy algorithm is employed to solve the corresponding optimization problem. Finally, comparison experiments are carried out for several synthetic networks and four real-world networks to demonstrate the effectiveness of the proposed approaches. •We propose a novel metric to evaluate network fragmentation.•We present a new nonconvex Mixed-Integer Quadratic Programming Model (MIQPM).•Computational results demonstrate the effectiveness of the proposed approaches.