{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:46:03Z","timestamp":1753875963236,"version":"3.41.2"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Portuguese funding agency, FCT - Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["UIDB\/50014\/2020"],"award-info":[{"award-number":["UIDB\/50014\/2020"]}]},{"name":"A Financial supervision and Technology compliance training programme\u2019","award":["825215"],"award-info":[{"award-number":["825215"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,10,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Community detection techniques use only the information about the network topology to find communities in networks. Similarly, classic clustering techniques for vector data consider only the information about the values of the attributes describing the objects to find clusters. In real-world networks, however, in addition to the information about the network topology, usually there is information about the attributes describing the vertices that can also be used to find communities. Using both the information about the network topology and about the attributes describing the vertices can improve the algorithms\u2019 results. Therefore, authors started investigating methods for community detection in attributed networks. In the past years, several methods were proposed to uncover this task, partitioning a graph into sub-graphs of vertices that are densely connected and similar in terms of their descriptions. This article focuses on the analysis and comparison of some of the proposed methods for community detection in attributed networks. For that purpose, several applications to both synthetic and real networks are conducted. Experiments are performed on both weighted and unweighted graphs. The objective is to establish which methods perform generally better according to the validation measures and to investigate their sensitivity to changes in the networks\u2019 structure and homogeneity.<\/jats:p>","DOI":"10.1093\/comnet\/cnaa044","type":"journal-article","created":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T20:15:11Z","timestamp":1604952911000},"source":"Crossref","is-referenced-by-count":4,"title":["New contributions for the comparison of community detection algorithms in attributed networks"],"prefix":"10.1093","volume":"8","author":[{"given":"Ana Rita","family":"Vieira","sequence":"first","affiliation":[{"name":"Faculdade de Economia, Universidade do Porto, Portugal"}]},{"given":"Pedro","family":"Campos","sequence":"additional","affiliation":[{"name":"Faculdade de Economia, Universidade do Porto & LIAAD - INESC TEC, Portugal"}]},{"given":"Paula","family":"Brito","sequence":"additional","affiliation":[{"name":"Faculdade de Economia, Universidade do Porto & LIAAD - INESC TEC, Portugal"}]}],"member":"286","published-online":{"date-parts":[[2020,12,31]]},"reference":[{"key":"2020123105195070900_B1","first-page":"75","article-title":"Community detection in graphs, Physics Reports","volume":"486","author":"Fortunato,","year":"2010","journal-title":"CoRR"},{"key":"2020123105195070900_B2","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/331499.331504","article-title":"Data clustering: a review","volume":"31","author":"Jain,","year":"1999","journal-title":"ACM Comuut. Surv."},{"key":"2020123105195070900_B3","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1145\/3184558.3191570","article-title":"Community detection in attributed network","author":"Falih,","year":"2018","journal-title":"Companion Proceedings of the The Web Conference 2018, WWW \u201918"},{"key":"2020123105195070900_B4","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1007\/s00180-018-0791-1","article-title":"ClustGeo: an R package for hierarchical clustering with spatial constraints","volume":"33","author":"Chavent,","year":"2018","journal-title":"Comput. Stat."},{"key":"2020123105195070900_B5","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1038\/s41598-017-02751-8","article-title":"Node attribute-enhanced community detection in complex networks","volume":"7","author":"Jia,","year":"2017","journal-title":"Sci. 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Mech.: Theory Exp."},{"key":"2020123105195070900_B9","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1007\/11569596_31","article-title":"Computing communities in large networks using random walks","author":"Pons,","year":"2005","journal-title":"Computer and Information Sciences - ISCIS 2005"},{"key":"2020123105195070900_B10","first-page":"7","article-title":"Community detection based on structural and attribute similarities","author":"Dang,","year":"2012","journal-title":"Proceedings of the Sixth International Conference on Digital Society"},{"key":"2020123105195070900_B11","first-page":"12:1","article-title":"Clustering large attributed graphs: a balance between structural and attribute similarities","volume":"5","author":"Cheng,","year":"2011","journal-title":"ACM Trans. Knowl. Discov. D."},{"key":"2020123105195070900_B12","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1007\/s10618-012-0263-0","article-title":"Clustering large attributed information networks: an efficient incremental computing approach","volume":"25","author":"Cheng,","year":"2012","journal-title":"Wires Data Min. 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