{"title":"Computational complexity of network vulnerability analysis","authors":"M. Berberler","doi":"10.2478/ausi-2022-0012","DOIUrl":null,"url":null,"abstract":"Abstract Residual closeness is recently proposed as a vulnerability measure to characterize the stability of complex networks. Residual closeness is essential in the analysis of complex networks, but costly to compute. Currently, the fastest known algorithms run in polynomial time. Motivated by the fast-growing need to compute vulnerability measures on complex networks, new algorithms for computing node and edge residual closeness are introduced in this paper. Those proposed algorithms reduce the running times to Θ(n3) and Θ (n4) on unweighted networks, respectively, where n is the number of nodes.","PeriodicalId":41480,"journal":{"name":"Acta Universitatis Sapientiae Informatica","volume":"21 1","pages":"199 - 207"},"PeriodicalIF":0.3000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Universitatis Sapientiae Informatica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/ausi-2022-0012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Residual closeness is recently proposed as a vulnerability measure to characterize the stability of complex networks. Residual closeness is essential in the analysis of complex networks, but costly to compute. Currently, the fastest known algorithms run in polynomial time. Motivated by the fast-growing need to compute vulnerability measures on complex networks, new algorithms for computing node and edge residual closeness are introduced in this paper. Those proposed algorithms reduce the running times to Θ(n3) and Θ (n4) on unweighted networks, respectively, where n is the number of nodes.