{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T04:08:48Z","timestamp":1750392528160,"version":"3.41.0"},"reference-count":34,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T00:00:00Z","timestamp":1618272000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072365","61772392"],"award-info":[{"award-number":["62072365","61772392"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence"],"published-print":{"date-parts":[[2021,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In a wide range of graph analysis tasks such as community detection and event detection, densest subgraph mining is important and primitive. With the development of social network, densest subgraph mining not only need to consider the structural data but also the attributes information, which descripts the features of nodes or edges. However, there are few researches on densest subgraph mining with attribute description. In this article, we only focus on the node\u2010attributed graph. According to the properties of structure and attribute in node\u2010attributed graphs, we define a novel dense subgraph pattern, called hybridized <jats:italic>k<\/jats:italic>\u2010truss in attribute\u2010augmented graph. A hybridized <jats:italic>k<\/jats:italic>\u2010truss is a subgraph that consists of structural nodes and attribute nodes, of which there are at least <jats:styled-content>(<jats:italic>k<\/jats:italic>\u2009\u2212\u20092)<\/jats:styled-content> common neighbors between any two connected nodes. We introduce the densest hybridized truss problem, and the densest hybridized truss mapping to a densely connected subgraph with homogenous attributes in the original graph. We propose a densest hybridized truss extraction (DHTE) algorithm for node\u2010attributed graphs, to automatically find the densest subgraph with high density and homogenous attributes at the same time. Extensive experimental results of 21 real world datasets demonstrate the effectiveness and efficiency of DHTE over state\u2010of\u2010the\u2010art methods, through comparison about structural cohesiveness and attributive homogeneity.<\/jats:p>","DOI":"10.1111\/coin.12448","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T14:01:05Z","timestamp":1618322465000},"page":"995-1010","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A truss\u2010based approach for densest homogeneous subgraph mining in node\u2010attributed graphs"],"prefix":"10.1111","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0818-0301","authenticated-orcid":false,"given":"Heli","family":"Sun","sequence":"first","affiliation":[{"name":"School of Journalism and New Media Xi'an Jiaotong University  Xi'an China"},{"name":"School of Computer Science and Technology Xi'an Jiaotong University  Xi'an China"}]},{"given":"Yawei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Xi'an Jiaotong University  Xi'an China"}]},{"given":"Xiaolin","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Xi'an Jiaotong University  Xi'an China"}]},{"given":"Pei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Xi'an Jiaotong University  Xi'an China"}]},{"given":"Ruodan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Xi'an Jiaotong University  Xi'an China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5588-3021","authenticated-orcid":false,"given":"Jianbin","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Xidian University  Xi'an China"}]},{"given":"Liang","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Xi'an Jiaotong University  Xi'an China"}]},{"given":"Zhongbin","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology China University of Mining and Technology  Xuzhou China"}]}],"member":"311","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/pmic.200400962"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkg340"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms8412"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.4337\/9781035304998.00010"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2005.01.027"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2032324100"},{"volume-title":"Trusses: Cohesive Subgraphs for Social Network Analysis.  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