Collective adaptation evolution of weighted complex networks: on syncronizability dependence

dc.authoridhttps://orcid.org/0000-0001-8766-6742en_US
dc.contributor.authorJing, Yuanwei
dc.contributor.authorWang, Dan
dc.contributor.authorDimirovski, Georgi M.
dc.date.accessioned2019-12-09T05:48:09Z
dc.date.available2019-12-09T05:48:09Z
dc.date.issued2017en_US
dc.departmentDoğuş Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionDimirovski, Georgi M. (Dogus Author) -- Conference full title: 13th IEEE International Conference on Control and Automation, ICCA 2017; Ohrid; Macedonia; 3 July 2017 through 6 July 2017.en_US
dc.description.abstractAn innovated evolving network representation model to characterize weighted, complex, scale-free networks is proposed. A new node or a community is added to network in the process of evolution while emergences of new links occur according to the 'Triad Formation' possessing symmetry and the random selection mechanism. A weighted scale-free network with high-value clustering coefficient can be obtained by adjusting two parameters only. The evolution of degree, strength, weights exhibit the power-law distributions. Highly correlated with the degree, the average strength displays scale-free property. The average clustering coefficient is found to exhibit well power-law decay as a function of the node degree. Triad Formation and Community Structure in weighted scale-free evolving networks, as building mechanisms, can distinctly enhance the clustering coefficient of networks. Both type I and type II networks are found their synchronizability to decrease as the average clustering coefficient increases in weighted scale-free networks of communities.en_US
dc.identifier.citationJing, Y., Wang, D., & Dimirovski, G. M. (2017). Collective adaptation evolution of weighted complex networks: on syncronizability dependence. In 13th IEEE International Conference on Control and Automation (pp. 88-93). Ohrid: IEEE. http://dx.doi.org/10.1109/ICCA.2017.8003040en_US
dc.identifier.doi10.1109/ICCA.2017.8003040
dc.identifier.endpage93en_US
dc.identifier.isbn9781538626795
dc.identifier.isbn9781538626788
dc.identifier.isbn9781538626801
dc.identifier.issn1948-3449
dc.identifier.issn1948-3457
dc.identifier.other17084127 (INSPEC)
dc.identifier.scopus2-s2.0-85029901857en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage88en_US
dc.identifier.urihttp://dx.doi.org/10.1109/ICCA.2017.8003040
dc.identifier.urihttps://hdl.handle.net/11376/3463
dc.identifier.wosWOS:000427123500016en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDimirovski, Georgi M.
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof13th IEEE International Conference on Control & Automationen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClustering Coefficienten_US
dc.subjectCollective Adaptationen_US
dc.subjectCommunity Structuresen_US
dc.subjectComplex Networksen_US
dc.titleCollective adaptation evolution of weighted complex networks: on syncronizability dependenceen_US
dc.typeConference Objecten_US

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