{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T06:57:06Z","timestamp":1762066626911,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,9,11]],"date-time":"2022-09-11T00:00:00Z","timestamp":1662854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Program of National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11922102","11871004","62141605","2018AAA0101100","2021YFB2700304"],"award-info":[{"award-number":["11922102","11871004","62141605","2018AAA0101100","2021YFB2700304"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["11922102","11871004","62141605","2018AAA0101100","2021YFB2700304"],"award-info":[{"award-number":["11922102","11871004","62141605","2018AAA0101100","2021YFB2700304"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Identifying the most influential spreaders in online social networks plays a prominent role in affecting information dissemination and public opinions. Researchers propose many effective identification methods, such as k-shell. However, these methods are usually validated by simulating propagation models, such as epidemic-like models, which rarely consider the Push-Republish mechanism with attenuation characteristic, the unique and widely-existing spreading mechanism in online social media. To address this issue, we first adopt the Push-Republish (PR) model as the underlying spreading process to check the performance of identification methods. Then, we find that the performance of classical identification methods significantly decreases in the PR model compared to epidemic-like models, especially when identifying the top 10% of superspreaders. Furthermore, inspired by the local tree-like structure caused by the PR model, we propose a new identification method, namely the Local-Forest (LF) method, and conduct extensive experiments in four real large networks to evaluate it. Results highlight that the Local-Forest method has the best performance in accurately identifying superspreaders compared with the classical methods.<\/jats:p>","DOI":"10.3390\/e24091279","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T22:53:46Z","timestamp":1663023226000},"page":"1279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Local-Forest Method for Superspreaders Identification in Online Social Networks"],"prefix":"10.3390","volume":"24","author":[{"given":"Yajing","family":"Hao","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Beihang University, Beijing 100191, China"},{"name":"Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoting","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing 100191, China"},{"name":"Institute of Artificial Intelligence, Beihang University, Beijing 100191, China"},{"name":"State Key Laboratory of Software Development Environment (NLSDE), Beihang University, Beijing 100191, China"},{"name":"Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China"},{"name":"PengCheng Laboratory, Shenzhen 518055, China"},{"name":"Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China"},{"name":"School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longzhao","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing 100191, China"},{"name":"Institute of Artificial Intelligence, Beihang University, Beijing 100191, China"},{"name":"Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China"},{"name":"PengCheng Laboratory, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Zheng","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China"},{"name":"Beijing Academy of Blockchain and Edge Computing (BABEC), Beijing 100085, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing 100191, China"},{"name":"Institute of Artificial Intelligence, Beihang University, Beijing 100191, China"},{"name":"Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China"},{"name":"PengCheng Laboratory, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing 100191, China"},{"name":"Institute of Artificial Intelligence, Beihang University, Beijing 100191, China"},{"name":"State Key Laboratory of Software Development Environment (NLSDE), Beihang University, Beijing 100191, China"},{"name":"Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China"},{"name":"PengCheng Laboratory, Shenzhen 518055, China"},{"name":"Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China"},{"name":"School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1109\/TEVC.2021.3081478","article-title":"Identifying influential spreaders in social networks through discrete moth-flame optimization","volume":"25","author":"Wang","year":"2021","journal-title":"IEEE Trans. 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