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A classification for community discovery methods in complex networks
Open accessAbstract
Many real‐world networks are intimately organized according to a community structure. Much research effort has been devoted to develop methods and algorithms that can efficiently highlight this hidden structure of a network, yielding a vast literature on what is called today community detection. Since network representation can be very complex and can contain different variants in the traditional graph model, each algorithm in the literature focuses on some of these properties and establishes, explicitly or implicitly, its own definition of community. According to this definition, each proposed algorithm then extracts the communities, which typically reflect only part of the features of real communities. The aim of this survey is to provide a ‘user manual’ for the community discovery problem. Given a meta definition of what a community in a social network is, our aim is to organize the main categories of community discovery methods based on the definition of community they adopt. Given a desired definition of community and the features of a problem (size of network, direction of edges, multidimensionality, and so on) this review paper is designed to provide a set of approaches that researchers could focus on. The proposed classification of community discovery methods is also useful for putting into perspective the many open directions for further research. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 512–546, 2011
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Citations
Cites
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 36,808
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Tensor Decompositions and Applications
Kolda, T. G., & Bader, B. W. (2009) · SIAM Review · doi:10.1137/07070111x · Cited by 10,301
A Set of Measures of Centrality Based on Betweenness
Freeman, L. C. (1977) · Sociometry · doi:10.2307/3033543 · Cited by 10,295
Finding community structure in very large networks
Clauset, A., Newman, M. E. J., & Moore, C. (2004) · Physical Review E · doi:10.1103/physreve.70.066111 · Cited by 7,390
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344
Maximizing the spread of influence through a social network
Kempe, D., Kleinberg, J., & Tardos, É. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956769 · Cited by 7,326
Modeling by shortest data description
Rissanen, J. (1978) · Automatica · doi:10.1016/0005-1098(78)90005-5 · Cited by 6,011
Fast algorithm for detecting community structure in networks
Newman, M. E. J. (2004) · Physical Review E · doi:10.1103/physreve.69.066133 · Cited by 5,406
Uncovering the overlapping community structure of complex networks in nature and society
Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005) · Nature · doi:10.1038/nature03607 · Cited by 5,373
An Efficient Heuristic Procedure for Partitioning Graphs
Kernighan, B. W., & Lin, S. (1970) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1970.tb01770.x · Cited by 5,305
Finding community structure in networks using the eigenvectors of matrices
Newman, M. E. J. (2006) · Physical Review E · doi:10.1103/physreve.74.036104 · Cited by 4,761
Maps of random walks on complex networks reveal community structure
Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583
Functional cartography of complex metabolic networks
Guimerà, R., & Nunes Amaral, L. A. (2005) · Nature · doi:10.1038/nature03288 · Cited by 3,983 · cited 1×
“Communities may correspond to groups of pages of the World Wide Web dealing with related topics [1], to functional modules such as cycles and pathways in metabolic networks [2, 3], to groups of related individuals in social networks [4] and so on.”
BackgroundAn Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
Near linear time algorithm to detect community structures in large-scale networks
Raghavan, U. N., Albert, R., & Kumara, S. (2007) · Physical Review E · doi:10.1103/physreve.76.036106 · Cited by 3,502
Numerical Methods for Least Squares Problems
Björck, Å. (1996) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611971484 · Cited by 3,257
Benchmark graphs for testing community detection algorithms
Lancichinetti, A., Fortunato, S., & Radicchi, F. (2008) · Physical Review E · doi:10.1103/physreve.78.046110 · Cited by 3,051
Resolution limit in community detection
Fortunato, S., & Barthélemy, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605965104 · Cited by 3,024
(2000) · CrossRef Listing of Deleted DOIs · doi:10.1162/jmlr.2003.3.4-5.993 · Cited by 3,024
Comparing community structure identification
Danon, L., Díaz-Guilera, A., Duch, J., & Arenas, A. (2005) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2005/09/p09008 · Cited by 2,872
Stochastic blockmodels: First steps
Holland, P. W., Laskey, K. B., & Leinhardt, S. (1983) · Social Networks · doi:10.1016/0378-8733(83)90021-7 · Cited by 2,755
Cost-effective outbreak detection in networks
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., & Glance, N. (2007) · Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1281192.1281239 · Cited by 2,502
Algorithm 457: finding all cliques of an undirected graph
Bron, C., & Kerbosch, J. (1973) · Communications of the ACM · doi:10.1145/362342.362367 · Cited by 2,452
Defining and identifying communities in networks
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400054101 · Cited by 2,450
Community detection algorithms: A comparative analysis
Lancichinetti, A., & Fortunato, S. (2009) · Physical Review E · doi:10.1103/physreve.80.056117 · Cited by 2,177
Hierarchical structure and the prediction of missing links in networks
Clauset, A., Moore, C., & Newman, M. E. J. (2008) · Nature · doi:10.1038/nature06830 · Cited by 2,171
Efficient influence maximization in social networks
Chen, W., Wang, Y., & Yang, S. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557047 · Cited by 2,156
Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010) · Science · doi:10.1126/science.1184819 · Cited by 2,123
Biclustering algorithms for biological data analysis: a survey
Madeira, S. C., & Oliveira, A. L. (2004) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2004.2 · Cited by 2,049
Statistical mechanics of community detection
Reichardt, J., & Bornholdt, S. (2006) · Physical Review E · doi:10.1103/physreve.74.016110 · Cited by 2,048
The dynamics of viral marketing
Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007) · ACM Transactions on the Web · doi:10.1145/1232722.1232727 · Cited by 2,032
Detecting community structure in networks
Newman, M. E. J. (2004) · The European Physical Journal B - Condensed Matter · doi:10.1140/epjb/e2004-00124-y · Cited by 2,028
Knowledge acquisition via incremental conceptual clustering
Fisher, D. H. (1987) · Machine Learning · doi:10.1007/bf00114265 · Cited by 1,991
Link communities reveal multiscale complexity in networks
Ahn, Y.-Y., Bagrow, J. P., & Lehmann, S. (2010) · Nature · doi:10.1038/nature09182 · Cited by 1,949
Detecting the overlapping and hierarchical community structure in complex networks
Lancichinetti, A., Fortunato, S., & Kertész, J. (2009) · New Journal of Physics · doi:10.1088/1367-2630/11/3/033015 · Cited by 1,941
Latent Space Approaches to Social Network Analysis
Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502388618906 · Cited by 1,917
A Framework for Clustering Evolving Data Streams
Aggarwal, C. C., Yu, P. S., Han, J., & Wang, J. (2003) · Proceedings 2003 VLDB Conference · doi:10.1016/b978-012722442-8/50016-1 · Cited by 1,808
Group formation in large social networks: membership, growth, and evolution
Backstrom, L., Huttenlocher, D., Kleinberg, J., & Lan, X. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150412 · Cited by 1,805
Partitioning Sparse Matrices with Eigenvectors of Graphs
Pothen, A., Simon, H. D., & Liou, K.-P. (1990) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/0611030 · Cited by 1,684
The Minimum Description Length Principle
Grünwald, P. D. (2007) · doi:10.7551/mitpress/4643.001.0001 · Cited by 1,644
Schaeffer, S. E. (2007) · Computer Science Review · doi:10.1016/j.cosrev.2007.05.001 · Cited by 1,639
Community detection in complex networks using extremal optimization
Duch, J., & Arenas, A. (2005) · Physical Review E · doi:10.1103/physreve.72.027104 · Cited by 1,549
Computing Communities in Large Networks Using Random Walks
Pons, P., & Latapy, M. (2006) · Journal of Graph Algorithms and Applications · doi:10.7155/jgaa.00124 · Cited by 1,501
Punctuated equilibrium and criticality in a simple model of evolution
Bak, P., & Sneppen, K. (1993) · Physical Review Letters · doi:10.1103/physrevlett.71.4083 · Cited by 1,497
Support vector machine learning for interdependent and structured output spaces
Tsochantaridis, I., Hofmann, T., Joachims, T., & Altun, Y. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015341 · Cited by 1,243
Community Structure in Directed Networks
Leicht, E. A., & Newman, M. E. J. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.118703 · Cited by 1,116
Self-organization and identification of Web communities
Flake, G. W., Lawrence, S., Giles, C. L., & Coetzee, F. M. (2002) · Computer · doi:10.1109/2.989932 · Cited by 1,018
Information-theoretic co-clustering
Dhillon, I. S., Mallela, S., & Modha, D. S. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956764 · Cited by 1,006
Performance of modularity maximization in practical contexts
Good, B. H., de Montjoye, Y.-A., & Clauset, A. (2010) · Physical Review E · doi:10.1103/physreve.81.046106 · Cited by 937
Empirical comparison of algorithms for network community detection
Leskovec, J., Lang, K. J., & Mahoney, M. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772755 · Cited by 918
Canonical analysis of several sets of variables
KETTENRING, J. R. (1971) · Biometrika · doi:10.1093/biomet/58.3.433 · Cited by 896
Finding overlapping communities in networks by label propagation
Gregory, S. (2010) · New Journal of Physics · doi:10.1088/1367-2630/12/10/103018 · Cited by 806
Relational learning via latent social dimensions
Tang, L., & Liu, H. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557109 · Cited by 799
Modularity and community detection in bipartite networks
Barber, M. J. (2007) · Physical Review E · doi:10.1103/physreve.76.066102 · Cited by 771
Cartography of complex networks: modules and universal roles
Guimerà, R., & Amaral, L. A. N. (2005) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2005/02/p02001 · Cited by 749
Detect overlapping and hierarchical community structure in networks
Shen, H., Cheng, X., Cai, K., & Hu, M.-B. (2009) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2008.12.021 · Cited by 723
Chakrabarti, D., Kumar, R., & Tomkins, A. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150467 · Cited by 661
Line graphs, link partitions, and overlapping communities
Evans, T. S., & Lambiotte, R. (2009) · Physical Review E · doi:10.1103/physreve.80.016105 · Cited by 615
Graph mining: Laws, generators, and algorithms
Chakrabarti, D., & Faloutsos, C. (2006) · ACM Computing Surveys · doi:10.1145/1132952.1132954 · Cited by 609
Mixture models and exploratory analysis in networks
Newman, M. E. J., & Leicht, E. A. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610537104 · Cited by 554
GraphScope: parameter-free mining of large time-evolving graphs
Sun, J., Faloutsos, C., Papadimitriou, S., & Yu, P. S. (2007) · Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1281192.1281266 · Cited by 532
A Framework for Projected Clustering of High Dimensional Data Streams
Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S. (2004) · Proceedings 2004 VLDB Conference · doi:10.1016/b978-012088469-8.50075-9 · Cited by 519
Identifying the influential bloggers in a community
Agarwal, N., Liu, H., Tang, L., & Yu, P. S. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341559 · Cited by 511
Stochastic blockmodels for directed graphs.
Wang, Y. J., & Wong, G. Y. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478385 · Cited by 480
Doreian, P., Batagelj, V., & Ferligoj, A. (2004) · Cambridge University Press eBooks · doi:10.1017/cbo9780511584176 · Cited by 472
Beyond streams and graphs: dynamic tensor analysis
Sun, J., Tao, D., & Faloutsos, C. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150445 · Cited by 462
Finding communities in linear time: a physics approach
Wu, F., & Huberman, B. A. (2004) · The European Physical Journal B - Condensed Matter · doi:10.1140/epjb/e2004-00125-x · Cited by 451
A framework for community identification in dynamic social networks
Tantipathananandh, C., Berger-Wolf, T., & Kempe, D. (2007) · Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1281192.1281269 · Cited by 443
Community detection in networks with positive and negative links
Traag, V. A., & Bruggeman, J. (2009) · Physical Review E · doi:10.1103/physreve.80.036115 · Cited by 434
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
Lin, Y.-R., Chi, Y., Zhu, S., Sundaram, H., & Tseng, B. L. (2008) · Proceedings of the 17th international conference on World Wide Web · doi:10.1145/1367497.1367590 · Cited by 407
Finding community structure in mega-scale social networks: [extended abstract]
Wakita, K., & Tsurumi, T. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242805 · Cited by 396
Inference and Phase Transitions in the Detection of Modules in Sparse Networks
Decelle, A., Krzakala, F., Moore, C., & Zdeborová, L. (2011) · Physical Review Letters · doi:10.1103/physrevlett.107.065701 · Cited by 392
Efficient and principled method for detecting communities in networks
Ball, B., Karrer, B., & Newman, M. E. J. (2011) · Physical Review E · doi:10.1103/physreve.84.036103 · Cited by 386
Finding Frequent Patterns in a Large Sparse Graph*
Kuramochi, M., & Karypis, G. (2005) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-005-0003-9 · Cited by 371
Scalable Tensor Decompositions for Multi-aspect Data Mining
Kolda, T. G., & Sun, J. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.89 · Cited by 358
Extending the definition of modularity to directed graphs with overlapping communities
Nicosia, V, Mangioni, G., Carchiolo, V, & Malgeri, M. (2009) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2009/03/p03024 · Cited by 346
Optimization with Extremal Dynamics
Boettcher, S., & Percus, A. G. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.5211 · Cited by 342
Modularity-maximizing graph communities via mathematical programming
Agarwal, G., & Kempe, D. (2008) · The European Physical Journal B · doi:10.1140/epjb/e2008-00425-1 · Cited by 338
Size reduction of complex networks preserving modularity
Arenas, A., Duch, J., Fernández, A., & Gómez, S. (2007) · New Journal of Physics · doi:10.1088/1367-2630/9/6/176 · Cited by 330
An Algorithm to Find Overlapping Community Structure in Networks
Gregory, S. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74976-9_12 · Cited by 302
Clique Relaxations in Social Network Analysis: The Maximum k-Plex Problem
Balasundaram, B., Butenko, S., & Hicks, I. V. (2011) · Operations Research · doi:10.1287/opre.1100.0851 · Cited by 302
Bayesian Approach to Network Modularity
Hofman, J. M., & Wiggins, C. H. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.258701 · Cited by 298
The Cohesiveness of Blocks In Social Networks: Node Connectivity and Conditional Density
White, D. R., & Harary, F. (2001) · Sociological Methodology · doi:10.1111/0081-1750.00098 · Cited by 296
Method to find community structures based on information centrality
Fortunato, S., Latora, V., & Marchiori, M. (2004) · Physical Review E · doi:10.1103/physreve.70.056104 · Cited by 294
Minimum Sum-Squared Residue Co-clustering of Gene Expression Data
Cho, H., Dhillon, I. S., Guan, Y., & Sra, S. (2004) · Proceedings of the 2004 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972740.11 · Cited by 293
Local method for detecting communities
Bagrow, J. P., & Bollt, E. M. (2005) · Physical Review E · doi:10.1103/physreve.72.046108 · Cited by 283
Analysis of community structure in networks of correlated data
Gómez, S., Jensen, P., & Arenas, A. (2009) · Physical Review E · doi:10.1103/physreve.80.016114 · Cited by 260
A framework for analysis of dynamic social networks
Berger-Wolf, T. Y., & Saia, J. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150462 · Cited by 253
Spectral clustering for multi-type relational data
Long, B., Zhang, Z. (., Wú, X., & Yu, P. S. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143918 · Cited by 251
Community evolution in dynamic multi-mode networks
Tang, L., Liu, H., Zhang, J., & Nazeri, Z. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401972 · Cited by 245
A particle-and-density based evolutionary clustering method for dynamic networks
Kim, M.-S., & Han, J. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687627.1687698 · Cited by 243
Fully automatic cross-associations
Chakrabarti, D., Papadimitriou, S., Modha, D. S., & Faloutsos, C. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014064 · Cited by 241
Scalable learning of collective behavior based on sparse social dimensions
Tang, L., & Liu, H. (2009) · Proceedings of the 18th ACM conference on Information and knowledge management · doi:10.1145/1645953.1646094 · Cited by 233
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
Uno, T., Kiyomi, M., & Arimura, H. (2005) · Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations · doi:10.1145/1133905.1133916 · Cited by 233
Community detection as an inference problem
Hastings, M. B. (2006) · Physical Review E · doi:10.1103/physreve.74.035102 · Cited by 213
MetaFac: community discovery via relational hypergraph factorization
Lin, Y.-R., Sun, J., Castro, P., Konuru, R., Sundaram, H., & Kelliher, A. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557080 · Cited by 211
Large scale multi-label classification via metalabeler
Tang, L., Rajan, S., & Narayanan, V. K. (2009) · Proceedings of the 18th international conference on World wide web · doi:10.1145/1526709.1526738 · Cited by 191
Discovering leaders from community actions
Goyal, A., Bonchi, F., & Lakshmanan, L. V. S. (2008) · Proceedings of the 17th ACM conference on Information and knowledge management · doi:10.1145/1458082.1458149 · Cited by 182
Berlingerio, M., Bonchi, F., Bringmann, B., & Gionis, A. (2009) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-04180-8_25 · Cited by 174
Community Mining from Multi-relational Networks
Cai, D., Shao, Z., He, X., Yan, X., & Han, J. (2005) · Lecture Notes in Computer Science · doi:10.1007/11564126_44 · Cited by 172
Uncoverning Groups via Heterogeneous Interaction Analysis
Tang, L., Wang, X., & Liu, H. (2009) · 2009 Ninth IEEE International Conference on Data Mining · doi:10.1109/icdm.2009.20 · Cited by 162
Unsupervised learning on k-partite graphs
Long, B., Xiaoyun, W., Zhang, Z. M., et al. (2006) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150439 · Cited by 148
Zhou, H., & Lipowsky, R. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24688-6_137 · Cited by 141
Efficient Identification of Overlapping Communities
Baumes, J., Goldberg, M., & Magdon‐Ismail, M. (2005) · Lecture notes in computer science · doi:10.1007/11427995_3 · Cited by 140
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Banerjee, A., Dhillon, I. S., Ghosh, J., Merugu, S., & Modha, D. S. (2004) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1014052.1014111 · Cited by 137
Temporal Analysis of Semantic Graphs Using ASALSAN
Bader, B. W., Harshman, R. A., & Kolda, T. G. (2007) · Proceedings · doi:10.1109/icdm.2007.54 · Cited by 130
Multi-way Clustering on Relation Graphs
Banerjee, A., Basu, S., & Merugu, S. (2007) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972771.14 · Cited by 128
Foundations of Multidimensional Network Analysis
Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., & Pedreschi, D. (2011) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2011.103 · Cited by 126
Self-citationFinding and characterizing communities in multidimensional networks
Berlingerio, M., Coscia, M., & Giannotti, F. (2011) · 2011 International Conference on Advances in Social Networks Analysis and Mining · doi:10.1109/asonam.2011.104 · Cited by 122
Self-citationFortunato, S., & Castellano, C. (2009) · Springer eBooks · doi:10.1007/978-0-387-30440-3_76 · Cited by 111
Fast agglomerative hierarchical clustering algorithm using Locality-Sensitive Hashing
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Information-theoretic approach to network modularity
Ziv, E., Middendorf, M., & Wiggins, C. H. (2005) · Physical Review E · doi:10.1103/physreve.71.046117 · Cited by 108
Finding Overlapping Communities Using Disjoint Community Detection Algorithms
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Blind Deconvolution of DS-CDMA Signals by Means of Decomposition in Rank-(1, L, L) Terms
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Window-based Tensor Analysis on High-dimensional and Multi-aspect Streams
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Hierarchical, Parameter-Free Community Discovery
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Constant-factor approximation algorithms for identifying dynamic communities
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HCDF: A Hybrid Community Discovery Framework
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Isolation concepts for efficiently enumerating dense subgraphs
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Detecting Overlapping Community Structures in Networks
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Unveiling community structures in weighted networks
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Finding redundant and complementary communities in multidimensional networks
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Metadata
- Authors
- Coscia, M., Giannotti, F., & Pedreschi, D.
- Venue
- Statistical Analysis and Data Mining: The ASA Data Science Journal
- Publisher
- Wiley
- Published
- 2011-09-09
- Volume
- 4
- Issue
- 5
- Pages
- 512-546
- ISSN
- 1932-1864 (ISSN-L)
- 1932-1872
- OpenAlex
- W2157527521
- Cited by
- 353
- References
- 139
- FWCI
- 14.44
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
