{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T02:23:37Z","timestamp":1775096617855,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T00:00:00Z","timestamp":1596153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>With the rapid development of social networks, it has become extremely important to evaluate the propagation capabilities of the nodes in a network. Related research has wide applications, such as in network monitoring and rumor control. However, the current research on the propagation ability of network nodes is mostly based on the analysis of the degree of nodes. The method is simple, but the effectiveness needs to be improved. Based on this problem, this paper proposes a method that is based on Tsallis entropy to detect the propagation ability of network nodes. This method comprehensively considers the relationship between a node\u2019s Tsallis entropy and its neighbors, employs the Tsallis entropy method to construct the TsallisRank algorithm, and uses the SIR (Susceptible, Infectious, Recovered) model for verifying the correctness of the algorithm. The experimental results show that, in a real network, this method can effectively and accurately evaluate the propagation ability of network nodes.<\/jats:p>","DOI":"10.3390\/e22080848","type":"journal-article","created":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T03:16:46Z","timestamp":1596424606000},"page":"848","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy"],"prefix":"10.3390","volume":"22","author":[{"given":"Xuegong","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 470075, China"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 470075, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5525-904X","authenticated-orcid":false,"given":"Zhifang","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 470075, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1282-5176","authenticated-orcid":false,"given":"Shengzong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, N., and Gillet, D. 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