Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
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Network Notation: Networks are often characterized by clusters of constituents that interact more closely with each other and have more connections to one another than they do with the rest of the components of the network. However, systematically identifying and studying such community structure in complicated networks is not easy, especially when the network interactions change over time or contain multiple types of connections, as seen in many biological regulatory networks or social networks. Mucha et al. (p. 876 ) developed a mathematical method to allow detection of communities that may be critical functional units of such networks. Application to real-world tasks—like making sense of the voting record in the U.S. Senate—demonstrated the promise of the method.
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The methodological advances used in this work are based on dynamic community detection techniques that have been tested and validated in a wide variety of simulated and empirical scenarios.
“The methodological advances used in this work are based on the dynamic community detection techniques, which were developed in the last few years (6), and have since been tested and validated in a wide variety of simulated and empirical scenarios.”
From Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015) · cites this paper as [6]
In the original paper (6), the methods were tested on two different kinds of social networks, and results were consistent with prior knowledge regarding these systems.
“In the original paper (6), the methods were tested on two different kinds of social networks, and results were consistent with prior knowledge regarding these systems.”
From Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015) · cites this paper as [6]
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Cites
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 · see citing works
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 · see citing works
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293 · see citing works
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 4,799 · see citing works
An Information Flow Model for Conflict and Fission in Small Groups
Zachary, W. W. (1977) · Journal of Anthropological Research · doi:10.1086/jar.33.4.3629752 · Cited by 4,709 · see citing works
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 · see citing works
Statistical mechanics of community detection
Reichardt, J., & Bornholdt, S. (2006) · Physical Review E · doi:10.1103/physreve.74.016110 · Cited by 2,048 · see citing works
Quantifying social group evolution
Palla, G., Barabási, A.-Ĺ., & Vicsek, T. (2007) · Nature · doi:10.1038/nature05670 · Cited by 1,679 · see citing works
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Li, Y., Liu, B., & Sarawagi, S. (2008) · doi:10.1145/1401890 · Cited by 1,209 · see citing works
Community Structure in Directed Networks
Leicht, E. A., & Newman, M. E. J. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.118703 · Cited by 1,118 · see citing works
Tastes, ties, and time: a new social network dataset using Facebook.com
Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A., & Christakis, N. (2008) · Social Networks · doi:10.1016/j.socnet.2008.07.002 · Cited by 1,024 · see citing works
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 · see citing works
Modularity and community detection in bipartite networks
Barber, M. J. (2007) · Physical Review E · doi:10.1103/physreve.76.066102 · Cited by 774 · see citing works
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 · see citing works
Size reduction of complex networks preserving modularity
Arenas, À., Duch, J., Fernández, A., & Gómez, S. (2007) · New Journal of Physics · doi:10.1088/1367-2630/9/6/176 · Cited by 330 · see citing works
Tracking evolving communities in large linked networks
Hopcroft, J. E., Khan, O., Kulis, B., & Selman, B. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307750100 · Cited by 299 · see citing works
Analysis of community structure in networks of correlated data
Gómez, S., Pablo Jensen, B., & Arenas, À. (2009) · Physical Review E · doi:10.1103/physreve.80.016114 · Cited by 261 · see citing works
A framework for analysis of dynamic social networks
Berger-Wolf, T. Y., & Saia, J. (2006) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150462 · Cited by 258 · see citing works
Spectral tripartitioning of networks
Richardson, T. S., Mucha, P. J., & Porter, M. A. (2009) · Physical Review E · doi:10.1103/physreve.80.036111 · Cited by 90 · see citing works
Self-citationFenn, D. J., Porter, M. A., McDonald, M., Williams, S., Johnson, N. F., & Jones, N. S. (2009) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.3184538 · Cited by 82 · see citing works
Self-citationThe Proceedings of the National Academy of Sciences
Wilson, E. B. (1915) · Science · doi:10.1126/science.42.1080.345 · Cited by 23 · see citing works
Stability of graph communities across time scalesPreprint
Delvenne, J., Yaliraki, S. N., & Barahona, M. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0812.1811 · Cited by 14 · see citing works
Smith, J. R., & Galvan, E. (2021) · doi:10.2172/1836402 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 16
- [4]M. E. J. Newman, Physical Review E 74 , 036104 (2006).
- [5]J. Reichardt, S. Bornholdt, Physical Review E 74 , 016110 (2006).
- [6]J. Hopcroft, O. Khan, B. Kulis, B. Selman, Proceedings of the National Academy of Sciences 101 , 5249 (2004).
- [11]T. M. Selee, T. G. Kolda, W. P. Kegelmeyer, J. D. Griffin, CSRI Summer Proceedings 2007, Technical Report SAND2007-7977, Sandia National Laboratories, Albuquerque, NM and Livermore, CA , M. L. Parks, S. S. Collis, eds. (2007), p. 87.
- [12]M. E. J. Newman, M. Girvan, Physical Review E 69 , 026113 (2004).
- [13]R. Lambiotte, J. C. Delvenne, M. Barahona, arXiv:0812.1770 (2008). · arXiv
- [15]M. J. Barber, Physical Review E 76 , 066102 (2007).
- [16]A. Arenas, J. Duch, A. Fernandez, S. Gomez, New Journal of Physics 9 , 176 (2007).
- [17]E. A. Leicht, M. E. J. Newman, Physical Review Letters 100 , 118703 (2008).
- [18]S. Gomez, P. Jensen, A. Arenas, Physical Review E 80 , 016114 (2009).
- [19]V. A. Traag, J. Bruggeman, Physical Review E 80 , 036115 (2009).
- [20]S. Fortunato, M. Barth´ elemy, Proceedings of the National Academy of Sciences 104 , 36 (2007).
- [21]B. H. Good, Y.-A. de Montjoye, A. Clauset, arXiv:0910.0165 (2009). · arXiv
- [22]W. W. Zachary, Journal of Anthropological Research 33 , 452 (1977).
- [23]A. S. Waugh, L. Pei, J. H. Fowler, P. J. Mucha, M. A. Porter, arXiv:0907.3509 (2009). · arXiv
- [25]T. Richardson, P. J. Mucha, M. A. Porter, Physical Review E 80 , 036111 (2009).
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Self-citationThe structure and dynamics of multilayer networks
Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., & Zanin, M. (2014) · Physics Reports · doi:10.1016/j.physrep.2014.07.001 · Cited by 3,125 · see citing works
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Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018985108 · Cited by 1,705 · see citing works
Self-citationIntrinsic and task-evoked network architectures of the human brain
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.014 · Cited by 1,564 · see citing works
Sporns, O., & Betzel, R. F. (2016) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033634 · Cited by 1,334 · see citing works
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Granell, C., Gómez, S., & Arenas, A. (2013) · Physical Review Letters · doi:10.1103/physrevlett.111.128701 · Cited by 1,064 · see citing works
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Gómez, S., Díaz-Guilera, A., Gómez-Gardeñes, J., Pérez-Vicente, C. J., Moreno, Y., & Arenas, A. (2013) · Physical Review Letters · doi:10.1103/physrevlett.110.028701 · Cited by 1,003 · see citing works
Dynamic reconfiguration of frontal brain networks during executive cognition in humans
Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., Schweiger, J. I., Grimm, O., Heinz, A., Tost, H., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422487112 · Cited by 791 · see citing works
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Bassett, D. S., Yang, M., Wymbs, N. F., & Grafton, S. T. (2015) · Nature Neuroscience · doi:10.1038/nn.3993 · Cited by 603 · see citing works
Structural reducibility of multilayer networks
De Domenico, M., Nicosia, V., Arenas, A., & Latora, V. (2015) · Nature Communications · doi:10.1038/ncomms7864 · Cited by 661 · see citing works
The physics of spreading processes in multilayer networks
De Domenico, M., Granell, C., Porter, M. A., & Arenas, A. (2016) · Nature Physics · doi:10.1038/nphys3865 · Cited by 649 · see citing works
Self-citationUnderstanding complexity in the human brain
Bassett, D. S., & Gazzaniga, M. S. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.03.006 · Cited by 572 · see citing works
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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 · see citing works
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Pessoa, L. (2014) · Physics of Life Reviews · doi:10.1016/j.plrev.2014.03.005 · Cited by 464 · see citing works
“Algorithms of community detection for multirelational networks offer exciting new avenues to explore network structure [152].”
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MUON: multimodal omics analysis framework
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Bagot, K. S., Matthews, S. A., Mason, M., Squeglia, L. M., Fowler, J., Gray, K., Herting, M., May, A., Colrain, I., Godino, J., et al. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.03.008 · Cited by 145 · see citing works
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Norton, H. K., Emerson, D. J., Huang, H., Kim, J., Titus, K. R., Gu, S., Bassett, D. S., & Phillips-Cremins, J. E. (2018) · Nature Methods · doi:10.1038/nmeth.4560 · Cited by 146 · see citing works
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Acuna, D. E., Wymbs, N. F., Reynolds, C. A., Picard, N., Turner, R. S., Strick, P. L., Grafton, S. T., & Kording, K. P. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00028.2014 · Cited by 54 · see citing works
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Fanuel, M., Alaíz, C. M., & Suykens, J. A. K. (2017) · Physical Review E · doi:10.1103/physreve.95.022302 · Cited by 50 · see citing works
Green, G. S., Yang, H., Fujita, M., Taga, M., Regev, A., Menon, V., Bennett, D. A. A., Habib, N., & De Jager, P. L. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.083212 · Cited by 51 · see citing works
Johnson, N. R., Yuan, P., Castillo, E., Lopez, T. P., Yue, W., Bond, A., Rivera, B. M., Sullivan, M. C., Hirouchi, M., Giles, K., et al. (2023) · Nature Communications · doi:10.1038/s41467-022-35753-w · Cited by 46 · see citing works
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Gilpin, D. R., & Miller, N. K. (2013) · Communication Theory · doi:10.1111/comt.12008 · Cited by 25 · see citing works
- Authors
- Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- University of Oxford (GB)
- Harvard University (US)
- Corresponding author
- Mucha, Peter J.
- Venue
- Science
- Published
- 2010-05-14
- Volume
- 328
- Issue
- 5980
- Pages
- 876-878
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2074617510
- Cited by
- 2,123
- References
- 24
- Citations by year
- 2026: 77 · 2025: 127 · 2024: 127 · 2023: 122 · 2022: 152 · 2021: 182 · 2020: 148 · 2019: 156 · 2018: 223 · 2017: 185 · 2016: 154 · 2015: 113 · 2014: 124 · 2013: 88 · 2012: 76
- FWCI
- 52.59
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (acceptedVersion)
- Oxford University Research Archive (ORA) (University of Oxford) (acceptedVersion)
- OpenSIUC (Southern Illinois University Carbondale) (submittedVersion)
- NCSU Libraries Repository (North Carolina State University Libraries) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- community structure, community detection, multiplex networks, time-dependent networks, multi-scale network, network evolution
- MeSH terms
- Algorithms, Group Processes, Humans, Interpersonal Relations, Models, Theoretical, Politics, Time Factors, United States, Friends, Population Groups
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