The DOI matches its Crossref record; 57 of the 59 references it cites were found in a registry or an index.
Finding and evaluating community structure in networks
Open accessAbstract
We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“After the pioneering PhenoGraph method (Levine et al, 2015), the standard approach to clustering single‐cell datasets has become multi‐resolution modularity optimization (Newman & Girvan, 2004; Reichardt & Bornholdt, 2006) as implemented in the Louvain algorithm (Blondel et al, 2008) on single‐cell KNN graphs.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Newman & Girvan, 2004
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1979) · Cited by 43,841
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Statistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Self-citationAdhi Harmoko, S., M.Komp, Jacquard, J. M., Zuse, K., & Eniac (2005) · Cited by 18,035
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
Self-citationA set of measures of centrality based upon betweenness
Freeman, L. (1977) · Sociometry · doi:10.2307/3033543 · Cited by 10,557
Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,236
Network Flows: Theory, Algorithms, and Applications.
Smith, D. K., Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1994) · Journal of the Operational Research Society · doi:10.2307/2583863 · Cited by 8,121
Social Network Analysis: A Handbook.
Pappi, F. U., & Scott, J. P. (1993) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2075047 · Cited by 5,964
Flagged first, then by citations
2 of the 59 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 101–200 of 1,029
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
Dynamical networks in tRNA:protein complexes
Sethi, A., Eargle, J., Black, A. A., & Luthey-Schulten, Z. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810961106 · Cited by 777
Age-related changes in modular organization of human brain functional networks
Meunier, D., Achard, S., Morcom, A., & Bullmore, E. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.062 · Cited by 756
Finding local community structure in networks
Clauset, A. J. (2005) · Physical Review E · doi:10.1103/physreve.72.026132 · Cited by 789
The development of human functional brain networks
Power, J. D., Fair, D. A., Schlaggar, B. L., & Petersen, S. E. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.017 · Cited by 751
Modularity and community detection in bipartite networks
Barber, M. J. (2007) · Physical Review E · doi:10.1103/physreve.76.066102 · Cited by 774
Compartmentalization increases food-web persistence
Stouffer, D. B., & Bascompte, J. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1014353108 · Cited by 751
A nonparametric view of network models and Newman–Girvan and other modularities
Bickel, P., & Chen, A. (2009) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0907096106 · Cited by 902
Defining and evaluating network communities based on ground-truth
Yang, J., & Leskovec, J. (2012) · doi:10.1145/2350190.2350193 · Cited by 792
Caldarelli, G. (2007) · OUP Catalogue · doi:10.1093/acprof:oso/9780199211517.001.0001 · Cited by 767
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 768
Decelle, A., Krząkała, F., Moore, C., & Zdeborová, L. (2011) · Physical Review E · doi:10.1103/physreve.84.066106 · Cited by 772
The network analysis of urban streets: A dual approach
Porta, S., Crucitti, P., & Latora, V. (2006) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2005.12.063 · Cited by 749
Schizophrenia, neuroimaging and connectomics
Fornito, A., Zalesky, A., Pantelis, C., & Bullmore, E. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.090 · Cited by 716
Graph analysis of the human connectome: Promise, progress, and pitfalls
Fornito, A., Zalesky, A., & Breakspear, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.087 · Cited by 743
Omar Alonso, A., Grobelnik, M., Jackson, P., & Thomson Corporation (2012) · Springer eBooks · doi:10.1007/springerreference_72921 · Cited by 740
Identifying the role that animals play in their social networks
Lusseau, D., & Newman, M. E. J. (2004) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rsbl.2004.0225 · Cited by 752
Self-citationDetect overlapping and hierarchical community structure in networks
Shen, H., Cheng, X., Cai, K., & Hu, M.-B. (2008) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2008.12.021 · Cited by 725
From hairballs to hypotheses–biological insights from microbial networks
Röttjers, L., & Faust, K. (2018) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuy030 · Cited by 742
Network Representation Learning: A Survey
Zhang, D., Yin, J., Zhu, X., & Zhang, C. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2850013 · Cited by 714
SPECTRAL CLUSTERING AND THE HIGH-DIMENSIONAL STOCHASTIC BLOCKMODEL
Rohe, K., Chatterjee, S., & Yu, B. (2010) · Cited by 782
Modeling cascading failures in the North American power grid
Kinney, R., Crucitti, P., Albert, R., & Latora, V. (2005) · The European Physical Journal B · doi:10.1140/epjb/e2005-00237-9 · Cited by 700
CitNetExplorer: A new software tool for analyzing and visualizing citation networks
van Eck, N. J., & Waltman, L. (2014) · Journal of Informetrics · doi:10.1016/j.joi.2014.07.006 · Cited by 738
The H-index of a network node and its relation to degree and coreness
Lü, L., Zhou, T., Zhang, Q.-M., & Stanley, E. (2016) · Nature Communications · doi:10.1038/ncomms10168 · Cited by 704
Complexity and stability of ecological networks: a review of the theory
Landi, P., Minoarivelo, H. O., Brännström, Å., Hui, C., & Dieckmann, U. (2018) · Population Ecology · doi:10.1007/s10144-018-0628-3 · Cited by 690
Statistical mechanics of networks
Park, J. M., & Newman, M. E. J. (2004) · Physical Review E · doi:10.1103/physreve.70.066117 · Cited by 684
Self-citationMislove, A. E., Viswanath, B., Gummadi, K. P., & Druschel, P. (2010) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1718487.1718519 · Cited by 669
Hierarchical modularity in human brain functional networks
David Meunier, A. (2009) · Frontiers in Neuroinformatics · doi:10.3389/neuro.11.037.2009 · Cited by 689
A new methodology for constructing a publication‐level classification system of science
Waltman, L., & van Eck, N. J. (2012) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.22748 · Cited by 700
Analysing ecological networks of species interactions
Delmas, E., Besson, M., Brice, M., Burkle, L. A., Riva, G. V. D., Fortin, M., Gravel, D., Guimarães, P. R., Hembry, D. H., Newman, E. A., et al. (2018) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12433 · Cited by 688
Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., Geček, S., Lipić, T., Boris Podobnik, A., Wang, L., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2021.10.005 · Cited by 688
Social media and political communication: a social media analytics framework
Stieglitz, S., & Dang-Xuan, L. (2012) · Social Network Analysis and Mining · doi:10.1007/s13278-012-0079-3 · Cited by 649
Graph theory methods: applications in brain networks
Sporns, O. (2018) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2018.20.2/osporns · Cited by 671
Gene network interconnectedness and the generalized topological overlap measure
Yip, A. M., & Horvath, S. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-22 · Cited by 633
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632
Efficient routing on complex networks
Yan, G. F., Zhou, T., Hu, B., Fu, Z., & Wang, B.-H. (2006) · Physical Review E · doi:10.1103/physreve.73.046108 · Cited by 632
Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans
Yong He, A., Wang, J., Wang, L. Y., Chen, Z. J., Yan, C., Yang, H., Tang, H., Zhu, C., Gong, Q., Zang, Y., et al. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005226 · Cited by 635
A method for detecting modules in quantitative bipartite networks
Dormann, C. F., & Strauss, R. (2013) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12139 · Cited by 639
Political Polarization on Twitter
Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Menczer, F., & Flammini, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14126 · Cited by 626
“Modularity (Newman and Girvan 2004) resulting from the cluster assignments offers a first measure of segregation , and reinforces the qualitative finding above.”
ResultclusterMaker: a multi-algorithm clustering plugin for Cytoscape
Morris, J. H., Apeltsin, L., Newman, A. M., Baumbach, J., Wittkop, T., Su, G., Bader, G. D., & Ferrin, T. E. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-436 · Cited by 631
Clustering and Community Detection in Directed Networks: A SurveyPreprint
Malliaros, F. D., & Vazirgiannis, M. (2016) · Cited by 637
Social network analysis: Characteristics of online social networks after a disaster
Kim, J., & Hastak, M. (2017) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2017.08.003 · Cited by 644
NetCoMi: network construction and comparison for microbiome data in R
Peschel, S., Müller, C. L., von Mutius, E., Boulesteix, A., & Depner, M. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa290 · Cited by 630
Community detection in Social Media
Papadopoulos, S., Kompatsiaris, I. Y., Vakali, A. I., & Spyridonos, P. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-011-0224-z · Cited by 615
Rat brains also have a default mode network
Lu, H., Zou, Q., Gu, H., Raichle, M. E., Stein, E. A., & Yang, Y. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1200506109 · Cited by 622
Boldi, P., Rosa, M., Santini, M., & Vigna, S. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963405.1963488 · Cited by 593
Clustering and community detection in directed networks: A survey
Malliaros, F. D., & Vazirgiannis, M. (2013) · Physics Reports · doi:10.1016/j.physrep.2013.08.002 · Cited by 594
Betzel, R. F., & Bassett, D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.006 · Cited by 587
Dynamics and Control of Diseases in Networks with Community Structure
Salathé, M., & Jones, J. H. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000736 · Cited by 586
Towards systemic and contextual priority setting for implementing the 2030 Agenda
Weitz, N., Carlsen, H., Nilsson, M., & Skånberg, K. (2017) · Sustainability Science · doi:10.1007/s11625-017-0470-0 · Cited by 574
Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy
Couturier, C., Ayyadhury, S., Le, P. U., Nadaf, J., Monlong, J., Riva, G., Allache, R., Baig, S., Yan, X., Bourgey, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17186-5 · Cited by 577
Innovation studies—The emerging structure of a new scientific field
Fagerberg, J., & Verspagen, B. (2009) · Research Policy · doi:10.1016/j.respol.2008.12.006 · Cited by 561
Analysis of the structure of complex networks at different resolution levels
Arenas, À., Fernández, A., & Gómez, S. (2008) · New Journal of Physics · doi:10.1088/1367-2630/10/5/053039 · Cited by 550
Fornito, A., Harrison, B. J., Zalesky, A., & Simons, J. S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1204185109 · Cited by 555
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Wang, Y., Cong, G., Song, G., & Xie, K. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835935 · Cited by 547
Spectral clustering and the high-dimensional stochastic blockmodel
Rohe, K., Chatterjee, S., & Yu, B. (2011) · The Annals of Statistics · doi:10.1214/11-aos887 · Cited by 537
Improved community detection in weighted bipartite networks
Beckett, S. J. (2016) · Royal Society Open Science · doi:10.1098/rsos.140536 · Cited by 543
Jun, S., Yoo, H. S., & Choi, S. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.11.009 · Cited by 587
Identification of overlapping community structure in complex networks using fuzzy -means clustering
Zhang, S., Wang, R., & Zhang, X.-S. (2006) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2006.07.023 · Cited by 531
Hierarchical Organization Unveiled by Functional Connectivity in Complex Brain Networks
Zhou, C., Zemanová, L., Zamora‐López, G., Hilgetag, C. C., & Kurths, J. (2006) · Physical Review Letters · doi:10.1103/physrevlett.97.238103 · Cited by 504
Social network analysis: An overview
Tabassum, S., Pereira, F. S. F., Fernandes, S., & Gama, J. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1256 · Cited by 581
Nestedness versus modularity in ecological networks: two sides of the same coin?
Fortuna, M. A., Stouffer, D. B., Olesen, J. M., Jordano, P., Mouillot, D., Krasnov, B. R., Poulin, R., & Bascompte, J. (2010) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2010.01688.x · Cited by 500
Community Discovery in Dynamic Networks
Rossetti, G., & Cazabet, R. (2018) · ACM Computing Surveys · doi:10.1145/3172867 · Cited by 532
Community detection in networks: A multidisciplinary review
Javed, M. A., Younis, M. S., Latif, S., Qadir, J., & Baig, A. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.02.011 · Cited by 519
A Comprehensive Survey on Community Detection With Deep Learning
Su, X., Xue, S., Liu, F., Wu, J., Yang, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Jin, D., et al. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3137396 · Cited by 545
Alexander-Bloch, A. F., Gogtay, N., Meunier, D., Birn, R., Clasen, L., Lalonde, F., Lenroot, R., Giedd, J., & Bullmore, E. T. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00147 · Cited by 480
Limits of modularity maximization in community detection
Lancichinetti, A., & Fortunato, S. (2011) · Physical Review E · doi:10.1103/physreve.84.066122 · Cited by 495
Querying k-truss community in large and dynamic graphs
Huang, X. S., Cheng, H., Qin, L., Tian, W., & Yu, J. X. (2014) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2588555.2610495 · Cited by 495
Nonlinear dynamics of multi-omics profiles during human aging
Shen, X., Wang, C., Zhou, X., Zhou, W., Hornburg, D., Wu, S., & Snyder, M. P. (2024) · Nature Aging · doi:10.1038/s43587-024-00692-2 · Cited by 500
GA-Net: A Genetic Algorithm for Community Detection in Social Networks
Pizzuti, C. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87700-4_107 · Cited by 491
Kortsch, S., Primicerio, R., Fossheim, M., Викторович, Д. А., & Aschan, M. (2015) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2015.1546 · Cited by 484
Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis
Donthu, N., Kumar, S., Pandey, N., Pandey, N., & Mishra, A. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.07.015 · Cited by 590
Mapping the Evolution of Social Research and Data Science on 30 Years of Social Indicators Research
Aria, M., Misuraca, M., & Spano, M. (2020) · Social Indicators Research · doi:10.1007/s11205-020-02281-3 · Cited by 562
Sun, F.-Y., Hoffmann, J., Verma, V., & Tang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.01000 · Cited by 727
Socially-aware routing for publish-subscribe in delay-tolerant mobile ad hoc networks
Costa, P., Cecilia Mascolo, C., Musolesi, M., & Picco, G. P. (2008) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2008.080602 · Cited by 478
Stability of graph communities across time scales
Delvenne, J., Yaliraki, S. N., & Barahona, M. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903215107 · Cited by 474
Detecting network communities: a new systematic and efficient algorithm
Donetti, L., & Muñoz, M. A. (2004) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2004/10/p10012 · Cited by 487
A method for analyzing text using VOSviewer
Bukar, U. A., Sayeed, M. S., Razak, A., Yogarayan, S., Amodu, O. A., & Mahmood, R. A. R. (2023) · MethodsX · doi:10.1016/j.mex.2023.102339 · Cited by 599
Informetrics at the beginning of the 21st century—A review
Bar‐Ilan, J. (2008) · Journal of Informetrics · doi:10.1016/j.joi.2007.11.001 · Cited by 490
The Convergence of Maturational Change and Structural Covariance in Human Cortical Networks
Alexander‐Bloch, A. F., Raznahan, A., Bullmore, E. T., & Giedd, J. N. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3554-12.2013 · Cited by 471
4 Consistency of Spectral Clustering in Stochastic Block ModelsPreprint
Lei, J., & Rinaldo, R. (2014) · Cited by 462
Crona, B., & Bodin, Ö. (2006) · Ecology and Society · doi:10.5751/es-01793-110207 · Cited by 458
Cognitive Network Neuroscience
Medaglia, J. D., Lynall, M.-E., & Bassett, D. S. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00810 · Cited by 419
Novák, P., Neumann, P., & Macas, J. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-378 · Cited by 458
Networks and the Epidemiology of Infectious Disease
Danon, L., Ford, A. P., House, T., Jewell, C., Keeling, M. J., Roberts, G. O., Ross, J. V., & Vernon, M. C. (2011) · Interdisciplinary Perspectives on Infectious Diseases · doi:10.48550/arxiv.1011.5950 · Cited by 457
Cognitive effort drives workspace configuration of human brain functional networks
Kitzbichler, M. G., Henson, R. N. A., Smith, M. L., Nathan, P. J., & Bullmore, E. T. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.0440-11.2011 · Cited by 427
Finding communities in linear time: a physics approach
Wu, F., & Huberman, B. A. (2004) · The European Physical Journal B · doi:10.1140/epjb/e2004-00125-x · Cited by 451
A framework for community identification in dynamic social networks
Tantipathananandh, C., Berger-Wolf, T. Y., & Kempe, D. (2007) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281269 · Cited by 449
Baum, G. L., Ciric, R., Roalf, D. R., Betzel, R. F., Moore, T. M., Shinohara, R. T., Kahn, A. E., Vandekar, S. N., Rupert, P. E., Quarmley, M., et al. (2017) · Current Biology · doi:10.1016/j.cub.2017.04.051 · Cited by 401
Efficient aggregation for graph summarization
Tian, Y., Hankins, R. A., & Patel, J. M. (2008) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1376616.1376675 · Cited by 444
Social network theory: new insights and issues for behavioral ecologists
Sih, A., Hanser, S. F., & McHugh, K. (2009) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-009-0725-6 · Cited by 459
Generalized Louvain method for community detection in large networks
Meo, P. D., Ferrara, E., Fiumara, G., & Provetti, A. (2011) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2011.6121636 · Cited by 442
Kumar, S., Sharma, D., Rao, S. K., Lim, W. M., & Mangla, S. K. (2022) · Annals of Operations Research · doi:10.1007/s10479-021-04410-8 · Cited by 497
Mapping the world of complex concentrated alloys
Gorsse, S., Miracle, D. B., & Senkov, O. N. (2017) · Acta Materialia · doi:10.1016/j.actamat.2017.06.027 · Cited by 437
Community detection in networks with positive and negative links
Traag, V., & Bruggeman, J. (2009) · Physical Review E · doi:10.1103/physreve.80.036115 · Cited by 435
Controlling edge dynamics in complex networks
Nepusz, T., & Vicsek, T. (2012) · Nature Physics · doi:10.1038/nphys2327 · Cited by 422
Classes of complex networks defined by role-to-role connectivity profiles
Guimerà, R., Sales‐Pardo, M., & Amaral, L. A. N. (2006) · Nature Physics · doi:10.1038/nphys489 · Cited by 422
Robust detection of dynamic community structure in networks
Bassett, D. S., Porter, M. A., Wymbs, N. F., Grafton, S. T., Carlson, J. M., & Mucha, P. J. (2013) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4790830 · Cited by 425
A Spectral Clustering Approach To Finding Communities in Graphs
White, S., & Smyth, P. (2005) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972757.25 · Cited by 423
Metadata
- Authors
- Newman, M. E. J., & Girvan, M.
- Affiliations
- Santa Fe Institute (US)
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2004-02-26
- Volume
- 69
- Issue
- 2
- Pages
- 026113-026113
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W2095293504
- Cited by
- 13,931
- References
- 59
- Citations by year
- 2026: 370 · 2025: 648 · 2024: 663 · 2023: 685 · 2022: 816 · 2021: 872 · 2020: 880 · 2019: 963 · 2018: 988 · 2017: 803 · 2016: 888 · 2015: 781 · 2014: 844 · 2013: 787 · 2012: 655
- FWCI
- 105.23
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review E (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- community structure, network clustering, edge deletion, betweenness centrality, number of communities, network partitioning, complex networks
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
