Remote control of cascading dynamics on complex multilayer networks

Abstract

To develop effective control strategies to enhance the robustness of multilayer networks against largescale failures is of significant value. We articulate the idea of 'remote control' whereby adaptive perturbations to one network layer are able to enhance the resilience of not only itself but also other interconnected network layers. We analyze the principle of remote control using percolation dynamics by showing analytically and numerically that, with the adaptive generation of a small number of new links in the control layer, not only is this layer but also other layers become dramatically more resistant to cascading failures. We also find that remote control is more effective for scale-free than for random networks. Remote intervention of multilayer network systems through adaptation has real-world applications, which we illustrate using the rail and coach transportation system in the Great Britain.

Community Detection Based on Graph Representation Learning in Evolutionary Networks

“…This is because the use of the deep learning model uses the historical structure of the network as the prior knowledge of the community detection at the current time, and the deep learning model obtains more input data when compared with the baselines. The research in community detection will further promote the development of the optimization of transportation network structures [30,31], the analysis of social networks [32,33], and research of biological systems [3,34].…”

A survey of community detection methods in multilayer networks

“…The study of detecting community structures in multilayer networks is experiencing a blossom in the last decade. Relevant researches cover various aspects among our daily life such as analyzing influential users in multiple social platforms (Al-Garadi et al 2018), finding organization of proteins in a biological system (Gosak et al 2018) and managing urban transportation system with various traffic manners (Liu et al 2019), etc. The following subsections summarized applications of community detection via a multilayer network framework.…”

Conserved Control Path in Multilayer Networks

“…Compared with the minimum set of driver nodes identified in single-layer networks, more drug targets occurred in a multilayer network [ 11 , 12 , 13 ]. Liu et al [ 14 ] demonstrated that remote control can enhance the robustness of multilayer transportation systems against cascading failure. Simultaneously, the controllability of multilayer networks has become increasingly important, and researchers have paid much attention to it.…”