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Clustering attributed graphs: Models, measures and methods

Published online by Cambridge University Press:  18 March 2015

CECILE BOTHOREL ,
JUAN DAVID CRUZ ,
MATTEO MAGNANI  and
BARBORA MICENKOVÁ
CECILE BOTHOREL
Affiliation:
Department of Logics in Uses, Social Science and Information Science, UMR CNRS 3192 Lab-STICC, Télécom Bretagne, Institut Mines-Télécom, Brest, France (e-mail: cecile.bothorel@telecom-bretagne.eu, juan.cruzgomez@telecom-bretagne.eu)
JUAN DAVID CRUZ
Affiliation:
Department of Logics in Uses, Social Science and Information Science, UMR CNRS 3192 Lab-STICC, Télécom Bretagne, Institut Mines-Télécom, Brest, France (e-mail: cecile.bothorel@telecom-bretagne.eu, juan.cruzgomez@telecom-bretagne.eu)
MATTEO MAGNANI
Affiliation:
Computing Science Division, IT Department, Uppsala University, Uppsala, Sweden (e-mail: matteo.magnani@uu.se)
BARBORA MICENKOVÁ
Affiliation:
Data Intensive Systems, Department of Computer Science, Aarhus University, Aarhus, Denmark (e-mail: barbora@cs.au.dk)
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Abstract

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 as attributed graphs. 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.

Keywords

attributed graphmultilayer networkclusteringcommunity detection

Information

Type
Research Article
Information
Network Science , Volume 3 , Issue 3 , September 2015 , pp. 408 - 444
Copyright
Copyright © Cambridge University Press 2015 

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References

Aggarwal, C., & Wang, H. (2010). Managing and mining graph data. USA: Springer.CrossRefGoogle Scholar
Akoglu, L., Tong, H., Meeder, B., & Faloutsos, C. (2012). Pics: Parameter-free identification of cohesive subgroups in large attributed graphs. Sdm. SIAM/Omnipress, pp. 439–450.CrossRefGoogle Scholar
Atzmueller, M., & Mitzlaff, F. (2011). Efficient descriptive community mining. Proceedings of the 24th International Flairs Conference. AAAI Press, pp. 459–464.Google Scholar
Balasubramanyan, R., & Cohen, W. W. (2011). Block-lda: Jointly modeling entity-annotated text and entity-entity links. Chap. 39, pp. 450–461.CrossRefGoogle Scholar
Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286 (5439), 509–512.CrossRefGoogle ScholarPubMed
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. 439–450.CrossRefGoogle Scholar
Ben-David, S., & Ackerman, M. (2008). Measures of clustering quality: A working set of axioms for clustering. Advances in Neural Information Processing Systems, pp. 121–128.Google Scholar
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. 490–494.CrossRefGoogle Scholar
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. 485–489.CrossRefGoogle Scholar
Berlingerio, M., Pinelli, F., & Calabrese, F. (2013). ABACUS: frequent pattern mining-based Community discovery in multidimensional networks. Data Min. Knowl. Discov., 27 (3), 294–320.CrossRefGoogle Scholar
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008 (10), P10008 (12pp).CrossRefGoogle Scholar
Boden, B., Günnemann, S., Hoffmann, H., & Seidl, T. (2012). Mining coherent subgraphs in multi-layer graphs with edge labels. Proceedings of the 18th ACM sigkdd International Conference on Knowledge Discovery and Data Mining - kdd '12. New York, USA: ACM Press, p. 1258.Google Scholar
Bonchi, F., Gionis, A., Gullo, F., & Ukkonen, A. (2012). Chromatic correlation clustering. Proceedings of the 18th ACM sigkdd International Conference on Knowledge Discovery and Data Mining - kdd '12. New York, USA: ACM Press, p. 1321.Google Scholar
Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323 (5916), 892–895.CrossRefGoogle ScholarPubMed
Brandes, U., Gaertler, M., & Wagner, D. (2008). Engineering graph clustering: Models and experimental evaluation. Journal of Experimental Algorithmics, 12, 1–26.Google Scholar
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. 497–501.CrossRefGoogle Scholar
Cai, D., Shao, Z., He, X., Yan, X., & Han, J. (2005). Mining hidden community in heterogeneous social networks. Proceedings of the 3rd International Workshop on Link Discovery - linkkdd '05. New York, USA: ACM Press, pp. 58–65.CrossRefGoogle Scholar
Combe, D., Largeron, C., Egyed-Zsigmond, E., & Géry, 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. 1280–1285.CrossRefGoogle Scholar
Coscia, M., Giannotti, F., & Pedreschi, D. (2011). A classification for community discovery methods in complex networks. Statistical Analysis and Data Mining, 4 (5), 512–546.CrossRefGoogle Scholar
Cruz, J. D., & Bothorel, C. (2013). Information integration for detecting communities in attributed graphs. Computational Aspects of Social Networks (cason), 62–67.CrossRefGoogle Scholar
Cruz, J. D., Bothorel, C., & Poulet, F. (2011a). Entropy based community detection in augmented social networks. Computational Aspects of Social Networks, 163–168.CrossRefGoogle Scholar
Cruz, J. D., Bothorel, C., & Poulet, F. (2011b). Semantic clustering of social networks using points of view. Coria: Conférence en Recherche d'information et Applications, 175–182.Google Scholar
Cruz, J. D., Bothorel, C., & Poulet, F. (2012). Détection et visualisation des communautés dans les réseaux sociaux. Revue d'intelligence Artificielle, 26 (4), 369–392.CrossRefGoogle Scholar
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. 1453–1454.CrossRefGoogle Scholar
Cruz, J. D., Bothorel, C., & Poulet, F. (2014). Community detection and visualization in social networks: Integrating structural and semantic information. ACM Transactions on Intelligent Systems and Technology, 5 (1), 11:1–11:26.Google Scholar
Dang, The Anh, & Viennet, E. (2012). Community detection based on structural and attribute similarities. International Conference on Digital Society (icds), 7–14. ISBN: 978-1-61208-176-2.Google Scholar
Danon, L., Díaz-Guilera, A., Duch, J., & Arenas, A. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005 (09), P09008.CrossRefGoogle Scholar
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486 (3–5), 75–174.CrossRefGoogle Scholar
Fortunato, S. & Barthélemy, M. (2007). Resolution limit in community detection. Proceedings of the National Academy of Sciences of the United States of America, 104 (1), 36–41.CrossRefGoogle ScholarPubMed
Freeman, L. C. (1996). Cliques, galois lattices, and the structure of human social groups. Social Networks, 18 (3), 173–187.CrossRefGoogle Scholar
Gaertler, M. (2005). Network analysis: Methodological foundations (pp. 178–215). Berlin/Heidelberg: Springer. Chap. Clustering.CrossRefGoogle Scholar
Gao, J., Buldyrev, S. V., Stanley, H. E., & Havlin, S. (2011). Networks formed from interdependent networks. Nature Physics, 8 (1), 40–48.CrossRefGoogle Scholar
Ge, R., Ester, M., Gao, B. J., Hu, Z., Bhattacharya, B., & Ben-Moshe, B. (2008). Joint cluster analysis of attribute data and relationship data: The connected k-center problem, algorithms and applications. ACM Transactions on Knowledge Discovery from Data, 2 (2), 7:1–7:35.CrossRefGoogle Scholar
Getoor, L., & Diehl, C. P. (2005). Link mining: A survey. SIGKDD Explorations Newsletter, 7 (2), 3–12.CrossRefGoogle Scholar
Goffman, E. (1974). Frame analysis: An essay on the organization of experience. Harper colophon books; CN 372. New York: Harper & Row.Google Scholar
Gong, N. Z., Talwalkar, A., Mackey, L. W., Huang, L., Shin, E. C. R., Stefanov, E., . . . Song, D. (2011). Jointly predicting links and inferring attributes using a social-attribute network (san). Corr, arXiv: abs/1112.3265.Google Scholar
Günnemann, S., Boden, B., Färber, I., & Seidl, T. (2013). Efficient mining of combined subspace and subgraph clusters in graphs with feature vectors. Advances in knowledge discovery and data mining (pp. 261–275). Springer.CrossRefGoogle Scholar
Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.) The Morgan Kaufmann Series in Data Management Systems. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.Google Scholar
Hanisch, D., Zien, A., Zimmer, R., & Lengauer, T. (2002). Co-clustering of biological networks and gene expression data. Bioinformatics, 18 (suppl 1), S145–S154.CrossRefGoogle ScholarPubMed
Hric, D., Darst, R. K., & Fortunato, S. (2014). Community detection in networks: Structural clusters versus ground truth. Physical Review E, 9, 062805.CrossRefGoogle Scholar
Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193–218. 10.1007/BF01908075.CrossRefGoogle Scholar
Jaccard, P. (1901). Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin del la société vaudoise des sciences naturelles, 37, 547–579.Google Scholar
Karger, D. R. (1993). Global min-cuts in rnc, and other ramifications of a simple min-out algorithm. Proceedings of the 4th Annual ACM-SIAM Symposium on Discrete Algorithms. SODA '93, Philadelphia, PA, USA: Society for Industrial and Applied Mathematics, pp. 21–30.Google Scholar
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, 407–412.CrossRefGoogle Scholar
Kim, M., & Leskovec, J. (2012). Multiplicative attribute graph model of real-world networks. Internet Mathematics, 8 (1–2), 113–160.CrossRefGoogle Scholar
Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 1–69.Google Scholar
Kohonen, T. (1997). Self-organizing maps. Secaucus, NJ, USA: Springer-Verlag New York, Inc.CrossRefGoogle Scholar
Kossinets, G., & Watts, D. J. (2006). Empirical analysis of an evolving social network. Science, 311 (5757), 88–90.CrossRefGoogle ScholarPubMed
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. 601–610.CrossRefGoogle Scholar
Lancichinetti, A., & Fortunato, S. (2011). Limits of modularity maximization in community detection. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 84 (6 Pt 2).Google ScholarPubMed
Lazega, E., & Pattison, P. E. (1999). Multiplexity, generalized exchange and cooperation in organizations: A case study. Social Networks, 21 (1), 67–90.CrossRefGoogle Scholar
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. 177–187.CrossRefGoogle Scholar
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. 631–640.CrossRefGoogle Scholar
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. 1203–1212.CrossRefGoogle Scholar
Li, W. J., & Yeung, D. Y. (2009). Relation regularized matrix factorization. IJCAI'09. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1126–1131.Google Scholar
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. 665–672.CrossRefGoogle Scholar
Magnani, M., Micenkova, B., & Rossi, L. (2013). Combinatorial analysis of multiple networks. arXiv:1303.4986 [cs.SI].Google Scholar
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. 5–12.Google Scholar
Magnani, M., & Rossi, L. (2013a). Formation of multiple networks. Social computing, behavioral-cultural modeling and prediction (pp. 257–264). Berlin, Heidelberg: Springer-Verlag.CrossRefGoogle Scholar
Magnani, M., & Rossi, L. (2013b). Pareto distance for multilayer network analysis. Social computing, behavioral-cultural modeling and prediction (pp. 249–256). Berlin, Heidelberg: Springer-Verlag.CrossRefGoogle Scholar
Minor, M. J. (1983). New directions in multiplexity analysis. Applied Network Analysis (pp. 223–244), Beverly Hills, CA, USA: Sage.Google Scholar
Moise, G., Zimek, A., Kröger, P., Kriegel, H.-P., & Sander, J. (2009). Subspace and projected clustering: Experimental evaluation and analysis. Knowledge and Information Systems, 21 (3), 299–326.CrossRefGoogle Scholar
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. 593–604.CrossRefGoogle Scholar
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328 (5980), 876–878.CrossRefGoogle ScholarPubMed
Nan, D., Wang, H., & Faloutsos, C. (2010). Analysis of large multi-modal social networks: Patterns and a generator. In Balcázar, J. L., Bonchi, F., Gionis, A., & Sebag, M. (Eds.), Machine learning and knowledge discovery in databases. Lecture Notes in Computer Science, vol. 6321. Berlin, Heidelberg: Springer, pp. 393–408.Google Scholar
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.Google Scholar
Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69 (2), 026113.Google ScholarPubMed
Nicosia, V., Mangioni, G., Carchiolo, V., & Malgeri, M. (2009). Extending the definition of modularity to directed graphs with overlapping communities. Journal of Statistical Mechanics: Theory and Experiment, 2009 (03), P03024.CrossRefGoogle Scholar
Noh, J. D., & Rieger, H. (2004). Random walks on complex networks. Physical Review Letters, 92 (11), 118701.CrossRefGoogle ScholarPubMed
Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435 (7043), 814–818.CrossRefGoogle ScholarPubMed
Pei, J., Jiang, D., & Zhang, A. (2005). On mining cross-graph quasi-cliques. Proceeding of the 11th ACM sigkdd International Conference on Knowledge Discovery in Data Mining - kdd '05. New York, USA: ACM Press, p. 228.Google Scholar
Pool, S., Bonchi, F. & van Leeuwen, M. (2014). Description-driven community detection. ACM Transactions on Intelligent Systems and Technology, 5 (2), 28:1–28:28.CrossRefGoogle Scholar
Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66 (336), 846–850.CrossRefGoogle Scholar
Rissanen, J. (1983). A universal prior for integers and estimation by minimum-description lenght. Annals of Statistics, 11 (2), 416–431.CrossRefGoogle Scholar
Rocklin, M., & Pinar, A. (2011). On clustering on graphs with multiple edge types. arxiv preprint, Sep.CrossRefGoogle Scholar
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. 979–986.CrossRefGoogle Scholar
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. 1089–1098.CrossRefGoogle Scholar
Schaeffer, S. E. (2007). Graph clustering. Computer Science Review, 1, 27–64.CrossRefGoogle Scholar
Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (8), 888–905.Google Scholar
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. 119–126.CrossRefGoogle Scholar
Skvoretz, J., & Agneessens, F. (2007). Reciprocity, multiplexity, and exchange: Measures. Quality & Quantity, 41 (3), 341–357.CrossRefGoogle Scholar
Stein, B., & Niggemann, O. (1999). On the nature of structure and its identification. Graph-theoretic concepts in computer science. (pp. 122–134). Springer-Verlag.CrossRefGoogle Scholar
Steinhaeuser, K., & Chawla, N. V. (2008). Community detection in a large real-world social network. In Liu, H., Salerno, J. J., & Young, M. J.. (Eds.), Social computing, behavioral modeling, and prediction (pp. 168–175). USA: Springer.CrossRefGoogle Scholar
Sun, Y., Han, J., Aggarwal, C. C., & Chawla, N. V. (2012). When will it happen? Proceedings of the 5th ACM International Conference on Web Search and Sata Mining - wsdm '12, New York, USA: ACM Press, p. 663.CrossRefGoogle Scholar
Tang, L., Wang, X., & Liu, H. (2011). Community detection via heterogeneous interaction analysis. Data Mining and Knowledge Discovery, 25 (1), 1–33.CrossRefGoogle Scholar
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. 747–756.CrossRefGoogle Scholar
van Laarhoven, T., & Marchiori, E. (2013). An axiomatic study of objective functions for graph clustering. Tech. rept. CoRR, abs/1308.3383.Google Scholar
Villa-Vialaneix, N., Olteanu, M., & Cierco-Ayrolles, C. (2013). Carte auto-organisatrice pour graphes étiquetés. Page Article numéro 4 of: Atelier Fouilles de Grands Graphes (FGG) - EGC'2013.Google Scholar
Vinh, N. X., Epps, J., & Bailey, J. (2010). Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. Journal of Machine Learning Research, 11, 2837–2854.Google Scholar
Wang, B., Cao, L., Suzuki, H., & Aihara, K. (2011). Epidemic spread in adaptive networks with multitype agents. Journal of Physics A: Mathematical and Theoretical, 44 (3), 035101.CrossRefGoogle Scholar
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. 73–73.CrossRefGoogle Scholar
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Structural analysis in the social sciences, vol. 8, no. 1., Cambridge University Press.CrossRefGoogle Scholar
Xie, J., Kelley, S., & Szymanski, B. K. (2013). Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Computing Surveys (csur), 45 (4), 43.CrossRefGoogle Scholar
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. 505–516.CrossRefGoogle Scholar
Yang, J., McAuley, J., & Leskovec, J. (2013). Community detection in networks with node attributes. IEEE 13th International Conference on Data Mining (icdm), 1151–1156.CrossRefGoogle Scholar
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. 537–546.CrossRefGoogle Scholar
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. 927–936.CrossRefGoogle Scholar
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. 1211–1212.CrossRefGoogle Scholar
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), 152–159.CrossRefGoogle Scholar
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. 853–864.CrossRefGoogle Scholar
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. 1007–1016.CrossRefGoogle Scholar
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. 797–802.Google Scholar
Zhou, Y., Cheng, H., & Yu, J. X. (2009). Graph clustering based on structural/attribute similarities. Proceedings of the VLDB Endowment, 2 (1), 718–729.CrossRefGoogle Scholar
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, 689–698.CrossRefGoogle Scholar