乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T11:11:38Z","timestamp":1743160298964},"reference-count":104,"publisher":"Cambridge University Press (CUP)","issue":"3","license":[{"start":{"date-parts":[[2015,3,18]],"date-time":"2015-03-18T00:00:00Z","timestamp":1426636800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Net Sci"],"published-print":{"date-parts":[[2015,9]]},"abstract":"Abstract<\/jats:title>Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models asattributed graphs<\/jats:italic>. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing, and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.<\/jats:p>","DOI":"10.1017\/nws.2015.9","type":"journal-article","created":{"date-parts":[[2015,3,18]],"date-time":"2015-03-18T05:59:41Z","timestamp":1426658381000},"page":"408-444","source":"Crossref","is-referenced-by-count":174,"title":["Clustering attributed graphs: Models, measures and methods"],"prefix":"10.1017","volume":"3","author":[{"given":"CECILE","family":"BOTHOREL","sequence":"first","affiliation":[]},{"given":"JUAN DAVID","family":"CRUZ","sequence":"additional","affiliation":[]},{"given":"MATTEO","family":"MAGNANI","sequence":"additional","affiliation":[]},{"given":"BARBORA","family":"MICENKOV\u00c1","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2015,3,18]]},"reference":[{"key":"S2050124215000090_ref96","doi-asserted-by":"crossref","unstructured":"Yang S.-H. , Long B. , Smola A. , Sadagopan N. , Zheng Z. , & Zha H. (2011). Like like alike: Joint friendship and interest propagation in social networks. Proceedings of the 20th International Conference on World Wide Web. WWW. ACM, pp. 537\u2013546.","DOI":"10.1145\/1963405.1963481"},{"key":"S2050124215000090_ref100","doi-asserted-by":"crossref","unstructured":"Zhao P. , Li X. , Xin D. , & Han J. (2011). Graph cube: On warehousing and olap multidimensional networks. Proceedings of the 2011 ACM Sigmod International Conference on Management of Data, ACM, pp. 853\u2013864.","DOI":"10.1145\/1989323.1989413"},{"key":"S2050124215000090_ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-013-0331-0"},{"key":"S2050124215000090_ref69","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.92.118701"},{"key":"S2050124215000090_ref98","doi-asserted-by":"crossref","unstructured":"Yin Z. , Gupta M. , Weninger T. , & Han J. (2010a). Linkrec: A unified framework for link recommendation with user attributes and graph structure. Proceedings of the 19th International conference on world wide web. WWW '10. New York, USA: ACM, pp. 1211\u20131212.","DOI":"10.1145\/1772690.1772879"},{"key":"S2050124215000090_ref51","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-8733(99)00002-7"},{"key":"S2050124215000090_ref83","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-77672-9_19"},{"key":"S2050124215000090_ref64","doi-asserted-by":"publisher","DOI":"10.1126\/science.1184819"},{"key":"S2050124215000090_ref61","first-page":"223","volume-title":"Applied Network Analysis","author":"Minor","year":"1983"},{"key":"S2050124215000090_ref99","doi-asserted-by":"crossref","unstructured":"Yin Z. , Gupta M. , Weninger T. , & Han J. (2010b). A unified framework for link recommendation using random walks. International Conference on Advances in Social Networks Analysis and Mining (asonam), 152\u2013159.","DOI":"10.1109\/ASONAM.2010.27"},{"key":"S2050124215000090_ref27","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2005\/09\/P09008"},{"key":"S2050124215000090_ref45","doi-asserted-by":"publisher","DOI":"10.1080\/15427951.2012.625257"},{"key":"S2050124215000090_ref36","unstructured":"Gong N. Z. , Talwalkar A. , Mackey L. W. , Huang L. , Shin E. C. R. , Stefanov E. , .\u00a0.\u00a0. Song D. (2011). Jointly predicting links and inferring attributes using a social-attribute network (san). Corr, arXiv: abs\/1112.3265."},{"key":"S2050124215000090_ref49","doi-asserted-by":"crossref","unstructured":"La Fond T. , & Neville J. (2010). Randomization tests for distinguishing social influence and homophily effects. Proceedings of the 19th International Conference on World Wide Web. WWW '10. New York, USA: ACM, pp. 601\u2013610.","DOI":"10.1145\/1772690.1772752"},{"key":"S2050124215000090_ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37210-0_28"},{"key":"S2050124215000090_ref11","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"S2050124215000090_ref89","first-page":"2837","article-title":"Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance","volume":"11","author":"Vinh","year":"2010","journal-title":"Journal of Machine Learning Research"},{"key":"S2050124215000090_ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2009.11.002"},{"key":"S2050124215000090_ref21","doi-asserted-by":"crossref","unstructured":"Cruz J. D. , Bothorel C. , & Poulet F. (2011a). Entropy based community detection in augmented social networks. Computational Aspects of Social Networks, 163\u2013168.","DOI":"10.1109\/CASON.2011.6085937"},{"key":"S2050124215000090_ref101","doi-asserted-by":"crossref","unstructured":"Zheleva E. , Sharara H. , & Getoor L. (2009). Co-evolution of social and affiliation networks. Proceedings of the 15th ACM sigkdd Conference on Knowledge Discovery and Data Mining (KDD), pp. 1007\u20131016.","DOI":"10.1145\/1557019.1557128"},{"key":"S2050124215000090_ref50","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.84.066122"},{"key":"S2050124215000090_ref3","unstructured":"Atzmueller M. , & Mitzlaff F. (2011). Efficient descriptive community mining. Proceedings of the 24th International Flairs Conference. AAAI Press, pp. 459\u2013464."},{"key":"S2050124215000090_ref17","doi-asserted-by":"publisher","DOI":"10.1145\/1134271.1134280"},{"key":"S2050124215000090_ref97","doi-asserted-by":"crossref","unstructured":"Yang T. , Jin R. , Chi Y. , & Zhu S. (2009). Combining link and content for community detection: A discriminative approach. KDD '09: Proceedings of the 15th ACM sigkdd International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, pp. 927\u2013936.","DOI":"10.1145\/1557019.1557120"},{"key":"S2050124215000090_ref71","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081898"},{"key":"S2050124215000090_ref33","doi-asserted-by":"publisher","DOI":"10.1145\/1376815.1376816"},{"key":"S2050124215000090_ref70","doi-asserted-by":"publisher","DOI":"10.1038\/nature03607"},{"key":"S2050124215000090_ref4","doi-asserted-by":"crossref","unstructured":"Balasubramanyan R. , & Cohen W. W. (2011). Block-lda: Jointly modeling entity-annotated text and entity-entity links. Chap. 39, pp. 450\u2013461.","DOI":"10.1137\/1.9781611972818.39"},{"key":"S2050124215000090_ref56","doi-asserted-by":"crossref","unstructured":"Liu Y. , Niculescu-Mizil A. , & Gryc W. (2009). Topic-link lda: Joint models of topic and author community. Proceedings of the 26th Annual International Conference on Machine Learning. ICML '09, New York, USA: ACM, pp. 665\u2013672.","DOI":"10.1145\/1553374.1553460"},{"key":"S2050124215000090_ref55","first-page":"1126","volume-title":"IJCAI'09","author":"Li","year":"2009"},{"key":"S2050124215000090_ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-97966-8"},{"key":"S2050124215000090_ref81","doi-asserted-by":"publisher","DOI":"10.1007\/s11135-006-9005-y"},{"key":"S2050124215000090_ref53","doi-asserted-by":"crossref","unstructured":"Leskovec J. , Lang K. J. , & Mahoney M. (2010). Empirical comparison of algorithms for network community detection. Proceedings of the 19th International Conference on World Wide Web. WWW '10, New York, USA: ACM, pp. 631\u2013640.","DOI":"10.1145\/1772690.1772755"},{"key":"S2050124215000090_ref90","doi-asserted-by":"publisher","DOI":"10.1088\/1751-8113\/44\/3\/035101"},{"key":"S2050124215000090_ref41","doi-asserted-by":"publisher","DOI":"10.1007\/BF01908075"},{"key":"S2050124215000090_ref67","article-title":"Finding and evaluating community structure in networks","volume":"69","author":"Newman","year":"2004","journal-title":"Physical Review E"},{"key":"S2050124215000090_ref65","first-page":"393","volume-title":"Machine learning and knowledge discovery in databases","author":"Nan","year":"2010"},{"key":"S2050124215000090_ref72","doi-asserted-by":"publisher","DOI":"10.1145\/2517088"},{"key":"S2050124215000090_ref7","unstructured":"Ben-David S. , & Ackerman M. (2008). Measures of clustering quality: A working set of axioms for clustering. Advances in Neural Information Processing Systems, pp. 121\u2013128."},{"key":"S2050124215000090_ref2","doi-asserted-by":"crossref","unstructured":"Akoglu L. , Tong H. , Meeder B. , & Faloutsos C. (2012). Pics: Parameter-free identification of cohesive subgroups in large attributed graphs. Sdm. SIAM\/Omnipress, pp. 439\u2013450.","DOI":"10.1137\/1.9781611972825.38"},{"key":"S2050124215000090_ref102","unstructured":"Zhiping Z. , & Jianyong W. (2006). Coherent closed quasi-clique discovery from large dense graph databases. Proceedings of the 12th ACM sigkdd International Conference on Knowledge Discovery and Data Mining - kdd '06, pp. 797\u2013802."},{"key":"S2050124215000090_ref5","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1126\/science.286.5439.509","article-title":"Emergence of scaling in random networks","volume":"286","author":"Barab\u00e1si","year":"1999","journal-title":"Science"},{"key":"S2050124215000090_ref23","doi-asserted-by":"publisher","DOI":"10.3166\/ria.26.369-392"},{"key":"S2050124215000090_ref14","doi-asserted-by":"publisher","DOI":"10.1126\/science.1165821"},{"key":"S2050124215000090_ref80","doi-asserted-by":"crossref","unstructured":"Silva A. , Meira W. Jr. , & Zaki M. J. (2010). Structural correlation pattern mining for large graphs. Proceedings of the 8th Workshop on Mining and Learning with Graphs. MLG '10, New York, USA: ACM, pp. 119\u2013126.","DOI":"10.1145\/1830252.1830268"},{"key":"S2050124215000090_ref104","doi-asserted-by":"crossref","unstructured":"Zhou Y. , Cheng H. , & Yu J. X. (2010). Clustering large attributed graphs: An efficient incremental approach. IEEE 10th International Conference on Data Mining (icdm). IEEE, 689\u2013698.","DOI":"10.1109\/ICDM.2010.41"},{"key":"S2050124215000090_ref30","doi-asserted-by":"publisher","DOI":"10.1016\/0378-8733(95)00271-5"},{"key":"S2050124215000090_ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37453-1_22"},{"key":"S2050124215000090_ref46","first-page":"1","article-title":"Multilayer networks","author":"Kivel\u00e4","year":"2014","journal-title":"Journal of Complex Networks"},{"key":"S2050124215000090_ref38","volume-title":"Data mining: Concepts and techniques","author":"Han","year":"2011"},{"key":"S2050124215000090_ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37210-0_27"},{"key":"S2050124215000090_ref87","unstructured":"van Laarhoven T. , & Marchiori E. (2013). An axiomatic study of objective functions for graph clustering. Tech. rept. CoRR, abs\/1308.3383."},{"key":"S2050124215000090_ref40","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.90.062805"},{"key":"S2050124215000090_ref15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1227161.1227162","article-title":"Engineering graph clustering: Models and experimental evaluation","volume":"12","author":"Brandes","year":"2008","journal-title":"Journal of Experimental Algorithmics"},{"key":"S2050124215000090_ref9","doi-asserted-by":"crossref","unstructured":"Berlingerio M. , Coscia M. , Giannotti F. , Monreale A. , & Pedreschi D. (2011b). Foundations of multidimensional network analysis. Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, IEEE, pp. 485\u2013489.","DOI":"10.1109\/ASONAM.2011.103"},{"key":"S2050124215000090_ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-6045-0"},{"key":"S2050124215000090_ref12","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339726"},{"key":"S2050124215000090_ref26","unstructured":"Dang The Anh , & Viennet E. (2012). Community detection based on structural and attribute similarities. International Conference on Digital Society (icds), 7\u201314. ISBN: 978-1-61208-176-2."},{"key":"S2050124215000090_ref77","doi-asserted-by":"crossref","unstructured":"Ruan Y. , Fuhry D. , & Parthasarathy S. (2013). Efficient community detection in large networks using content and links. Proceedings of the 22nd International Conference on World Wide Web. WWW '13, pp. 1089\u20131098.","DOI":"10.1145\/2488388.2488483"},{"key":"S2050124215000090_ref79","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"S2050124215000090_ref24","doi-asserted-by":"crossref","unstructured":"Cruz J. D. , Bothorel C. , & Poulet F. (2013). Integrating heterogeneous information within a social network for detecting communities. Proceedings of the IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining. New York, USA: ACM, pp. 1453\u20131454.","DOI":"10.1145\/2492517.2492588"},{"key":"S2050124215000090_ref78","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2007.05.001"},{"key":"S2050124215000090_ref43","first-page":"21","volume-title":"Proceedings of the 4th Annual ACM-SIAM Symposium on Discrete Algorithms","author":"Karger","year":"1993"},{"key":"S2050124215000090_ref76","doi-asserted-by":"crossref","unstructured":"Rossetti G. , Berlingerio M. , & Giannotti F. (2011). Scalable link prediction on multidimensional networks. Proceedings of the IEEE 11th International Conference on Data Mining Workshops, IEEE, pp. 979\u2013986.","DOI":"10.1109\/ICDMW.2011.150"},{"key":"S2050124215000090_ref52","doi-asserted-by":"crossref","unstructured":"Leskovec J. , Kleinberg J. , & Faloutsos C. (2005). Graphs over time: Densification laws, shrinking diameters and possible explanations. Proceedings of the 11th ACM sigkdd International Conference on Knowledge Discovery in Data Mining. ACM, pp. 177\u2013187.","DOI":"10.1145\/1081870.1081893"},{"key":"S2050124215000090_ref34","doi-asserted-by":"publisher","DOI":"10.1145\/1117454.1117456"},{"key":"S2050124215000090_ref62","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-009-0226-y"},{"key":"S2050124215000090_ref39","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/18.suppl_1.S145"},{"key":"S2050124215000090_ref84","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124373"},{"key":"S2050124215000090_ref95","doi-asserted-by":"crossref","unstructured":"Yang J. , McAuley J. , & Leskovec J. (2013). Community detection in networks with node attributes. IEEE 13th International Conference on Data Mining (icdm), 1151\u20131156.","DOI":"10.1109\/ICDM.2013.167"},{"key":"S2050124215000090_ref66","unstructured":"Neville J. , Adler M. , & Jensen D. D. (2003). Clustering relational data using attribute and link information. Proceedings of the Workshop on Text Mining and Link Analysis, 18th International Joint Conference on Artificial Intelligence."},{"key":"S2050124215000090_ref6","doi-asserted-by":"crossref","unstructured":"Barbieri N. , Bonchi F. , & Manco G. (2013). Cascade-based community detection. Proceedings of the 6th ACM International Conference on Web Search and Data Mining. WSDM '13, New York, USA: ACM, pp. 439\u2013450.","DOI":"10.1145\/2433396.2433403"},{"key":"S2050124215000090_ref29","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0605965104"},{"key":"S2050124215000090_ref94","doi-asserted-by":"crossref","unstructured":"Xu Z. , Ke Y. , Wang Y. , Cheng H. , & Cheng J. (2012). A model-based approach to attributed graph clustering. Proceedings of the 2012 ACM Sigmod International Conference on Management of Data. SIGMOD '12, New York, USA: ACM, pp. 505\u2013516.","DOI":"10.1145\/2213836.2213894"},{"key":"S2050124215000090_ref75","doi-asserted-by":"crossref","unstructured":"Rocklin M. , & Pinar A. (2011). On clustering on graphs with multiple edge types. arxiv preprint, Sep.","DOI":"10.1007\/978-3-642-21286-4_4"},{"key":"S2050124215000090_ref32","doi-asserted-by":"publisher","DOI":"10.1038\/nphys2180"},{"key":"S2050124215000090_ref19","doi-asserted-by":"publisher","DOI":"10.1002\/sam.10133"},{"key":"S2050124215000090_ref68","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2009\/03\/P03024"},{"key":"S2050124215000090_ref58","unstructured":"Magnani M. , & Rossi L. (2011). The ML-model for multi layer network analysis. IEEE International Conference on Advances in Social Network Analysis and Mining, IEEE Computer Society, Los Alamitos, pp. 5\u201312."},{"key":"S2050124215000090_ref74","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176346150"},{"key":"S2050124215000090_ref73","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1971.10482356"},{"key":"S2050124215000090_ref35","volume-title":"Frame analysis: An essay on the organization of experience","author":"Goffman","year":"1974"},{"key":"S2050124215000090_ref91","doi-asserted-by":"crossref","unstructured":"Wang J. , Zhou Z. , & Lizhu Z. (2006). CLAN: An algorithm for mining closed cliques from large dense graph databases. Proceedings of the 22nd International Conference on Data Engineering (icde'06). IEEE, pp. 73\u201373.","DOI":"10.1109\/ICDE.2006.34"},{"key":"S2050124215000090_ref86","doi-asserted-by":"crossref","unstructured":"Tong H. , Faloutsos C. , & Koren Y. (2007). Fast direction-aware proximity for graph mining. Proceedings of the 13th ACM sigkdd International Conference on Knowledge Discovery and Data Mining. KDD '07, New York, USA: ACM, pp. 747\u2013756.","DOI":"10.1145\/1281192.1281272"},{"key":"S2050124215000090_ref93","doi-asserted-by":"publisher","DOI":"10.1145\/2501654.2501657"},{"key":"S2050124215000090_ref48","doi-asserted-by":"publisher","DOI":"10.1126\/science.1116869"},{"key":"S2050124215000090_ref18","doi-asserted-by":"crossref","unstructured":"Combe D. , Largeron C. , Egyed-Zsigmond E. , & G\u00e9ry M. (2012). Combining relations and text in scientific network clustering. Proceedings of the IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE, pp. 1280\u20131285.","DOI":"10.1109\/ASONAM.2012.215"},{"key":"S2050124215000090_ref25","first-page":"11:1","article-title":"Community detection and visualization in social networks: Integrating structural and semantic information","volume":"5","author":"Cruz","year":"2014","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"S2050124215000090_ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-31955-9_8"},{"key":"S2050124215000090_ref8","doi-asserted-by":"crossref","unstructured":"Berlingerio M. , Coscia M. , & Giannotti F. (2011a). Finding and characterizing communities in multidimensional networks. Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, IEEE, pp. 490\u2013494.","DOI":"10.1109\/ASONAM.2011.104"},{"key":"S2050124215000090_ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339735"},{"key":"S2050124215000090_ref42","first-page":"547","article-title":"\u00c9tude comparative de la distribution florale dans une portion des Alpes et des Jura","volume":"37","author":"Jaccard","year":"1901","journal-title":"Bulletin del la soci\u00e9t\u00e9 vaudoise des sciences naturelles"},{"key":"S2050124215000090_ref82","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-46784-X_13"},{"key":"S2050124215000090_ref85","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-011-0231-0"},{"key":"S2050124215000090_ref22","unstructured":"Cruz J. D. , Bothorel C. , & Poulet F. (2011b). Semantic clustering of social networks using points of view. Coria: Conf\u00e9rence en Recherche d'information et Applications, 175\u2013182."},{"key":"S2050124215000090_ref57","unstructured":"Magnani M. , Micenkova B. , & Rossi L. (2013). Combinatorial analysis of multiple networks. arXiv:1303.4986 [cs.SI]."},{"key":"S2050124215000090_ref92","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511815478"},{"key":"S2050124215000090_ref63","doi-asserted-by":"crossref","unstructured":"Moser F. , Colak R. , Rafiey A. , & Ester M. (2009). Mining cohesive patterns from graphs with feature vectors. SIAM Data Mining Conf (SDM), Chap. 50, pp. 593\u2013604.","DOI":"10.1137\/1.9781611972795.51"},{"key":"S2050124215000090_ref44","doi-asserted-by":"crossref","unstructured":"Kazienko P. , Brodka P. , Musial K. , & Gaworecki J. (2010). Multi-layered social network creation based on bibliographic data. 2010 IEEE 2nd International Conference on Social Computing, IEEE, 407\u2013412.","DOI":"10.1109\/SocialCom.2010.65"},{"key":"S2050124215000090_ref88","unstructured":"Villa-Vialaneix N. , Olteanu M. , & Cierco-Ayrolles C. (2013). Carte auto-organisatrice pour graphes \u00e9tiquet\u00e9s. Page Article num\u00e9ro 4 of: Atelier Fouilles de Grands Graphes (FGG) - EGC'2013."},{"key":"S2050124215000090_ref16","doi-asserted-by":"crossref","unstructured":"Brodka P. , Stawiak P. , & Kazienko P. (2011). Shortest path discovery in the multi-layered social network. Proceedings of the International Conference on Advances in Social Networks Analysis and Mining. IEEE, pp. 497\u2013501.","DOI":"10.1109\/ASONAM.2011.67"},{"key":"S2050124215000090_ref20","doi-asserted-by":"crossref","unstructured":"Cruz J. D. , & Bothorel C. (2013). Information integration for detecting communities in attributed graphs. Computational Aspects of Social Networks (cason), 62\u201367.","DOI":"10.1109\/CASoN.2013.6622601"},{"key":"S2050124215000090_ref54","doi-asserted-by":"crossref","unstructured":"Li H. , Nie Z. , Lee W.-C. , Giles C. L. , & Wen J.-R. (2008). Scalable community discovery on textual data with relations. Proceedings of the ACM Conference on Information and Knowledge Management (cikm 2008), pp. 1203\u20131212.","DOI":"10.1145\/1458082.1458241"},{"key":"S2050124215000090_ref103","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687709"}],"container-title":["Network Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S2050124215000090","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T22:20:37Z","timestamp":1717798837000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S2050124215000090\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,3,18]]},"references-count":104,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2015,9]]}},"alternative-id":["S2050124215000090"],"URL":"https:\/\/doi.org\/10.1017\/nws.2015.9","relation":{},"ISSN":["2050-1242","2050-1250"],"issn-type":[{"value":"2050-1242","type":"print"},{"value":"2050-1250","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,3,18]]}}}