{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T15:50:53Z","timestamp":1762876253911,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:00:00Z","timestamp":1614643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009625","name":"Beijing Social Science Fund","doi-asserted-by":"publisher","award":["11JGB063"],"award-info":[{"award-number":["11JGB063"]}],"id":[{"id":"10.13039\/501100009625","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Social Science Research Project of Ministry of Education","award":["11YJA630109"],"award-info":[{"award-number":["11YJA630109"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Identifying and ranking the node influence in complex networks is an important issue. It helps to understand the dynamics of spreading process for designing efficient strategies to hinder or accelerate information spreading. The idea of decomposing network to rank node influence is adopted widely because of low computational complexity. Of this type, decomposition is a dynamic process, and each iteration could be regarded as an inverse process of spreading. In this paper, we propose a new ranking method, Dynamic Node Strength Decomposition, based on decomposing network. The spreading paths are distinguished by weighting the edges according to the nodes at both ends. The change of local structure in the process of decomposition is considered. Our experimental results on four real networks with different sizes show that the proposed method can generate a more monotonic ranking list and identify node influence more effectively.<\/jats:p>","DOI":"10.3390\/a14030082","type":"journal-article","created":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T21:24:01Z","timestamp":1614720241000},"page":"82","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Identifying and Ranking Influential Nodes in Complex Networks Based on Dynamic Node Strength"],"prefix":"10.3390","volume":"14","author":[{"given":"Xu","family":"Li","sequence":"first","affiliation":[{"name":"Applied Economics, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1693-0857","authenticated-orcid":false,"given":"Qiming","family":"Sun","sequence":"additional","affiliation":[{"name":"Management Science and Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1038\/nature08932","article-title":"Catastrophic Cascade of Failures in Interdependent Networks","volume":"464","author":"Buldyrev","year":"2010","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.ecolmodel.2007.04.029","article-title":"Ecological network analysis: Network construction","volume":"208","author":"Fath","year":"2017","journal-title":"Ecol. 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