{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T13:59:44Z","timestamp":1720533584602},"reference-count":19,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2015,4]]},"abstract":"<jats:p> In this paper, we proposed an edge weight method for performing a community detection on mixed scale-free networks.We use the phrase \u201cmixed scale-free networks\u201d for networks where some communities have node degree that follows a power law similar to scale-free networks, while some have node degree that follows normal distribution. In this type of network, community detection algorithms that are designed for scale-free networks will have reduced accuracy because some communities do not have scale-free properties. On the other hand, algorithms that are not designed for scale-free networks will also have reduced accuracy because some communities have scale-free properties. To solve this problem, our algorithm consists of two community detection steps; one is aimed at extracting communities whose node degree follows power law distribution (scale-free), while the other one is aimed at extracting communities whose node degree follows normal distribution (non scale-free). To evaluate our method, we use NMI \u2014 Normalized Mutual Information \u2014 to measure our results on both synthetic and real-world datasets comparing with both scale-free and non scale-free community detection methods. The results show that our method outperforms all other based line methods on mixed scale-free networks. <\/jats:p>","DOI":"10.1142\/s0218213015400072","type":"journal-article","created":{"date-parts":[[2015,4,13]],"date-time":"2015-04-13T05:06:31Z","timestamp":1428901591000},"page":"1540007","source":"Crossref","is-referenced-by-count":3,"title":["Edge Weight Method for Community Detection on Mixed Scale-Free Networks"],"prefix":"10.1142","volume":"24","author":[{"given":"Sorn","family":"Jarukasemratana","sequence":"first","affiliation":[{"name":"Tokyo Institute of Technology, W8-59 2-12-1 Ookayama, Meguro-ku, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tsuyoshi","family":"Murata","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, W8-59 2-12-1 Ookayama, Meguro-ku, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2015,4,13]]},"reference":[{"key":"p_1","doi-asserted-by":"publisher","DOI":"10.1038\/scientificamerican0503-60"},{"issue":"5","key":"p_3","first-page":"056122","volume":"70","author":"Bogua M.","year":"2004","journal-title":"Rev. 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