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Communities, or clusters, are groups of vertices having higher probability of being connected to each other than to the members in other groups. Considering the importance of triangle structures, we first propose \u03c3<\/jats:italic>\u2010tensor to model ordinary relationships and triangle relationships simultaneously. Then, we propose a simple but effective latent factor prior, ie, latent factor cosine similarity prior, to improve community detection. The latent factor cosine similarity prior is a kind of statistics of the well\u2010defined synthetic multi\u2010relational social networks. It is based on a key observation that most latent feature factors of intra\u2010group members in these networks are highly similar according to cosine similarity measure. Using this prior along with the RESCAL tensor factorization model, we can obtain a superior latent feature factor matrix. Moreover, N<\/jats:italic>\u2010RESCAL model, a variant of RESCAL model, and its corresponding algorithm N<\/jats:italic>\u2010RESCAL\u2010ALS are proposed for the simplicity and the removal of the limit of cosine similarity. Once the latent factor matrix is obtained by factorizing \u03c3<\/jats:italic>\u2010tensor using N<\/jats:italic>\u2010RESCAL model, we apply agglomerative clustering algorithm for community discovery. We call this framework as TNRA. Experiment results on several real\u2010world datasets are surprisingly promising, clearly demonstrating the power of the proposed prior and the effectiveness of our proposed methods.<\/jats:p>","DOI":"10.1002\/cpe.4453","type":"journal-article","created":{"date-parts":[[2018,3,23]],"date-time":"2018-03-23T06:32:15Z","timestamp":1521786735000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Using triangles and latent factor cosine similarity prior to improve community detection in multi\u2010relational social networks"],"prefix":"10.1002","volume":"30","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-8985-2573","authenticated-orcid":false,"given":"Jianzhou","family":"Zhan","sequence":"first","affiliation":[{"name":"College of Engineering Shantou University Shantou China"}]},{"given":"Mei","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Engineering Shantou University Shantou China"}]},{"given":"Huidan","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Engineering Shantou University Shantou China"}]},{"given":"Haojun","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Engineering Shantou University Shantou China"}]}],"member":"311","published-online":{"date-parts":[[2018,3,22]]},"reference":[{"issue":"1","key":"e_1_2_9_2_1","article-title":"Identifying community structure in a multi\u2010relational network employing non\u2010negative tensor factorization and GA k\u2010means clustering","volume":"7","author":"Verma A","year":"2017","journal-title":"Wiley Interdiscip Rev Data Min Knowl Disc"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15105-7_6"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.4876436"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.37"},{"key":"e_1_2_9_6_1","doi-asserted-by":"crossref","unstructured":"CantiniL MedicoE FortunatoS CaselleM.Detection of gene communities in multi\u2010networks reveals cancer drivers. arXiv preprint arXiv:1507.08415;2015.","DOI":"10.1038\/srep17386"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.72.027104"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2005.04.022"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2009.11.002"},{"key":"e_1_2_9_10_1","unstructured":"GligorijevicV PanagakisY ZafeiriouS.Non\u2010negative matrix factorizations for multiplex network analysis. arXiv preprint arXiv:1612.00750;2016."},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2854006.2854013"},{"key":"e_1_2_9_12_1","unstructured":"PapalexakisEE AkogluL IenceD.Do more views of a graph help? 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