{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:12:39Z","timestamp":1772043159279,"version":"3.50.1"},"reference-count":103,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T00:00:00Z","timestamp":1582243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61433014"],"award-info":[{"award-number":["61433014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673085"],"award-info":[{"award-number":["61673085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science Strength Promotion Programme of UESTC","award":["Y03111023901014006"],"award-info":[{"award-number":["Y03111023901014006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes\u2019 spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.<\/jats:p>","DOI":"10.3390\/e22020242","type":"journal-article","created":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T10:49:16Z","timestamp":1582282156000},"page":"242","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":117,"title":["Influential Nodes Identification in Complex Networks via Information Entropy"],"prefix":"10.3390","volume":"22","author":[{"given":"Chungu","family":"Guo","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of Electricity Science and Technology of China, Chengdu 611731, China"}]},{"given":"Liangwei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electricity Science and Technology of China, Chengdu 611731, China"}]},{"given":"Xiao","family":"Chen","sequence":"additional","affiliation":[{"name":"Information Assurance Office of Army Staff, Beijing 100043, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2239-3012","authenticated-orcid":false,"given":"Duanbing","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electricity Science and Technology of China, Chengdu 611731, China"},{"name":"The Center for Digital Culture and Media, University of Electricity Science and Technology of China, Chengdu 611731, China"},{"name":"Institute of Fundamental and Frontier Sciences, University of Electricity Science and Technology of China, Chengdu 611731, China"},{"name":"Union Big Data Tech. Inc., Chengdu 610041, China"}]},{"given":"Hui","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electricity Science and Technology of China, Chengdu 611731, China"}]},{"given":"Jing","family":"Ma","sequence":"additional","affiliation":[{"name":"Business School, Sichuan University, Chengdu 610064, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.4236\/ce.2018.99109","article-title":"The Scientific Collaboration Networks in University Management in Brazil","volume":"9","author":"Silva","year":"2018","journal-title":"Creat. Educ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"065103","DOI":"10.1103\/PhysRevE.68.065103","article-title":"Self-similar community structure in a network of human interactions","volume":"68","author":"Guimera","year":"2003","journal-title":"Phys. Rev. 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