2023/10/10 by Peng Peng, Tianlong Fan, Peng, Peng +5
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · Psychology · #05C80 (Secondary) #05C82 (Primary) #91D30 #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #FOS: Physical sciences #I.5.1 #J.2 #Mental Health Research Topics #Physics and Society (physics.soc-ph)
paper · pdf · doi:10.48550/arxiv.2310.06407
openalex publication_date 2023/10/10 · openalex created_date 2023/10/13 · openalex updated_date 2026/07/28
Edges, binding together nodes within networks, have the potential to induce dramatic transitions when specific collective failure behaviors emerge. These changes, initially unfolding covertly and then erupting abruptly, pose substantial, unforeseeable threats to networked systems, and are termed explosive vulnerability. Thus, identifying influential edges capable of triggering such drastic transitions, while minimizing cost, is of utmost significance. Here, we address this challenge by introducing edge collective influence (ECI), which builds upon the optimal percolation theory applied to line graphs. ECI embodies features of both optimal and explosive percolation, involving minimized removal costs and explosive dismantling tactic. Furthermore, we introduce two improved versions of ECI, namely IECI and IECIR, tailored for objectives of hidden and fast dismantling, respectively, with their superior performance validated in both synthetic and empirical networks. Finally, we present a dual competitive percolation (DCP) model, whose reverse process replicates the explosive dismantling process and the trajectory of the cost function of ECI, elucidating the microscopic mechanisms enabling ECI's optimization. ECI and the DCP model demonstrate the profound connection between optimal and explosive percolation. This work significantly deepens our comprehension of percolation and provides valuable insights into the explosive vulnerabilities arising from edge collective behaviors.