{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T17:08:51Z","timestamp":1765040931356,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,25]],"date-time":"2019-05-25T00:00:00Z","timestamp":1558742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Startup Research Fund for Ph.D of Liaoning Province, China","award":["20170520364"],"award-info":[{"award-number":["20170520364"]}]},{"name":"Key R\\&amp;D Program Guidance Plan of Liaoning Province, China","award":["2018104013"],"award-info":[{"award-number":["2018104013"]}]},{"name":"Technological Innovation Program for Young People of Shenyang City","award":["RC180338"],"award-info":[{"award-number":["RC180338"]}]},{"name":"Science and Technology Program of Shenyang City, China","award":["F16-155-9-00, and F17-180-9-00"],"award-info":[{"award-number":["F16-155-9-00, and F17-180-9-00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The real world is full of rich and valuable complex networks. Community structure is an important feature in complex networks, which makes possible the discovery of some structure or hidden related information for an in-depth study of complex network structures and functional characteristics. Aimed at community detection in complex networks, this paper proposed a membrane algorithm based on a self-organizing map (SOM) network. Firstly, community detection was transformed as discrete optimization problems by selecting the optimization function. Secondly, three elements of the membrane algorithm, objects, reaction rules, and membrane structure were designed to analyze the properties and characteristics of the community structure. Thirdly, a SOM was employed to determine the number of membranes by learning and mining the structure of the current objects in the decision space, which is beneficial to guiding the local and global search of the proposed algorithm by constructing the neighborhood relationship. Finally, the simulation experiment was carried out on both synthetic benchmark networks and four real-world networks. The experiment proved that the proposed algorithm had higher accuracy, stability, and execution efficiency, compared with the results of other experimental algorithms.<\/jats:p>","DOI":"10.3390\/e21050533","type":"journal-article","created":{"date-parts":[[2019,5,26]],"date-time":"2019-05-26T23:07:27Z","timestamp":1558912047000},"page":"533","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A SOM-Based Membrane Optimization Algorithm for Community Detection"],"prefix":"10.3390","volume":"21","author":[{"given":"Chuang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Engineering, Shenyang University, Liaoning 110044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingkui","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shenyang University, Liaoning 110044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahao","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shenyang University, Liaoning 110044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1109\/TEVC.2017.2737600","article-title":"Evolutionary Computation for Community Detection in Networks: A Review","volume":"22","author":"Pizzuti","year":"2018","journal-title":"IEEE Trans. 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