{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:42:53Z","timestamp":1702600973906},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684703","type":"print"},{"value":"9781643684710","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,12]]},"abstract":"<jats:p>Key nodes are important for analyzing the evolution of time-varying networks. Persistent homology can calculate topological information of the network in different dimensions and encode it into a persistence diagram. This paper first defines the Topological Similarity (TS) distance based on the theory of persistent homology to describe the topological similarity of the networks. The smaller the TS distance, the more similar the network is. Secondly, an algorithm for identifying key nodes based on TS distance (KITS) is proposed to obtain the sequence of key nodes in time-varying network. Finally, the paper analyses the TS distance on two real time-varying social networks and compares it with the degree centrality. The experimental results show that the TS distance can effectively characterize the topological similarity between two networks and that the KITS algorithm accurately and comprehensively identifies key nodes.<\/jats:p>","DOI":"10.3233\/faia231025","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:06:18Z","timestamp":1702566378000},"source":"Crossref","is-referenced-by-count":0,"title":["Identifying Key Nodes Based on TS Distance in Time-Varying Social Networks"],"prefix":"10.3233","author":[{"given":"Yayun","family":"Liu","sequence":"first","affiliation":[{"name":"Faculty of Science, Kunming University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Science, Kunming University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Jiang","sequence":"additional","affiliation":[{"name":"Faculty of Science, Kunming University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Zhong","sequence":"additional","affiliation":[{"name":"Faculty of Science, Kunming University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyang","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Science, Kunming University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining IX"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231025","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:06:19Z","timestamp":1702566379000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231025"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"ISBN":["9781643684703","9781643684710"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231025","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,12]]}}}