Percolation behaviors of finite components on complex networks. (1st April 2022)

• Record Type: Journal Article
• Authors: Wang, Fan; Dong, Gaogao; Tian, Lixin; Stanley, H Eugene
• Is Part Of: New journal of physics. Volume 24:Number 4(2022)
• Publication Date: 2022-04-01
• Subjects: complex networks -- percolation behaviors -- network resilience; Physics -- Periodicals; Physics; Periodicals; 530.05
• DOI: 10.1088/1367-2630/ac5f86
• Languages: English
• ISSNs: 1367-2630

Abstract

Abstract: Percolation behavior is of wide applicability and provides insight into functional structure of complex networks. Different from the percolation behaviors of giant component (GC), the finite components make one more clearly explore network percolation behaviors and critical phenomena from a microscopic perspective, especially for large-scale network systems. Here we focus on the percolation behaviors of small component π s with the size s = 1, 2, 3, … under different failure scenarios such as random attack, localized attack, target attack and intentional attack with limited knowledge. We find theoretically and via simulation that finite components all show the peak shape which is different from GC for random networks including random regular network, Erdős–R e ´ nyi networks and scale-free networks. In particular, we find a new general scaling relationship between 1 s and 1 p max, p max represents the value of p (non-failure fraction of initial nodes) corresponding to the peak point of π s in the network. This finding also provides a potential approach for determining the critical threshold and fill the gap between finite components and GC on the percolation process.