A graphical criterion for the controllability in temporal networks

Jin-cheng Tu, Hou-qing Lu, Tian-ming Lu, Zong-qiao Xie, Lei Lu and Lingxiang Wei

Physica A: Statistical Mechanics and its Applications, 2024, vol. 646, issue C

Abstract: Link temporality is a fundamental characteristic of diverse real networks across various domains, posing challenges in comprehending and controlling complex systems. The ultimate goal of understanding complex systems is to effectively manipulate them from their initial state to a desired state. Previous studies have predominantly focused on static networks due to computational complexity in analyzing temporal networks. In this study, we aim to enhance our understanding of link temporality and propose a graphical criterion for evaluating the dimension of controllable space in temporal systems by utilizing maximum matching in their aggregated static counterparts. This criterion overcomes the computation constraints associated with controlling large networks. We validated our graphical criterion in multiple temporal networks including model and real temporal networks, and observed that temporal networks with more degree-homogeneous snapshots are easier to be controlled. Moreover, we revealed that temporal process can disrupt the linear dependence of signals and found that the pivotal role of leaf links in expanding the dimension of controllable space in temporal networks.

Keywords: Network controllability; Temporal networks; Degree-heterogeneous networks; Control space prediction (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:646:y:2024:i:c:s0378437124004151

DOI: 10.1016/j.physa.2024.129906

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