The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Quantifying social group evolution
Open accessAbstract
The rich set of interactions between individuals in society results in complex community structure, capturing highly connected circles of friends, families or professional cliques in a social network. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimization of society as a whole. We have developed an algorithm based on clique percolation that allows us to investigate the time dependence of overlapping communities on a large scale, and thus uncover basic relationships characterizing community evolution. Our focus is on networks capturing the collaboration between scientists and the calls between mobile phone users. We find that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability. The behaviour of small groups displays the opposite tendency—the condition for stability is that their composition remains unchanged. We also show that knowledge of the time commitment of members to a given community can be used for estimating the community’s lifetime. These findings offer insight into the fundamental differences between the dynamics of small groups and large institutions.
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.
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
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
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
Self-citationDefining 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
Dorogovtsev, S. N., & Mendes, J. F. F. (2003) · doi:10.1093/acprof:oso/9780198515906.001.0001 · Cited by 1,563
Cited by
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
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Statistical physics of social dynamics
Castellano, C., Fortunato, S., & Loreto, V. (2009) · Reviews of Modern Physics · doi:10.1103/revmodphys.81.591 · Cited by 4,119
Vicsek, T., & Zafeiris, A. (2012) · Physics Reports · doi:10.1016/j.physrep.2012.03.004 · Cited by 2,923
Self-citationHolme, P., & Saramäki, J. (2012) · Physics Reports · doi:10.1016/j.physrep.2012.03.001 · Cited by 2,736
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
Community detection in networks: a user guide
Fortunato, S., & Hric, D. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.09.002 · Cited by 2,086
Dynamic reconfiguration of human brain networks during learning
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
Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., Petersen, A. M., Radicchi, F., Sinatra, R., Uzzi, B., et al. (2018) · Science · doi:10.1126/science.aao0185 · Cited by 1,497
“Size is a crucial determinant of team survival strategies: Small teams survive longer if they maintain a stable core, but larger teams persist longer if they manifest a mechanism for membership turnover (56).”
Self-citationDistinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
Aral, S., Muchnik, L., & Sundararajan, A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0908800106 · Cited by 1,207
Dynamic social networks promote cooperation in experiments with humans
Rand, D. G., Arbesman, S., & Christakis, N. A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1108243108 · Cited by 653
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
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
Group Faultlines: A Review, Integration, and Guide to Future Research
Thatcher, S. M. B., & Patel, P. C. (2012) · Journal of Management · doi:10.1177/0149206311426187 · Cited by 391
Time as a limited resource: Communication strategy in mobile phone networks
Miritello, G., Moro, E., Lara, R., Martínez-López, R., Belchamber, J., Roberts, S. G. B., & Dunbar, R. I. M. (2013) · Social Networks · doi:10.1016/j.socnet.2013.01.003 · Cited by 203
Persistence and uncertainty in the academic career
Petersen, A. M., Riccaboni, M., Stanley, H. E., & Pammolli, F. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121429109 · Cited by 188
Ego-Network Stability and Innovation in Alliances
Kumar, P., & Zaheer, A. (2019) · Academy of Management Journal · doi:10.5465/amj.2016.0819 · Cited by 171
A comparative study of ecological specialization estimators
Poisot, T., Canard, E., Mouquet, N., & Hochberg, M. E. (2012) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2011.00174.x · Cited by 156
Algorithmic Transference: People Overgeneralize Failures of AI in the Government
Longoni, C., Cian, L., & Kyung, E. J. (2023) · Journal of Marketing Research · doi:10.1177/00222437221110139 · Cited by 121
Gaumont, N., Panahi, M., & Chavalarias, D. (2018) · PLOS ONE · doi:10.1371/journal.pone.0201879 · Cited by 88
Complex societies and the growth of the law
Katz, D. M., Coupette, C., Beckedorf, J., & Hartung, D. (2020) · Scientific Reports · doi:10.1038/s41598-020-73623-x · Cited by 55
Focus-shifting patterns of OSS developers and their congruence with call graphs
Xuan, Q., Okano, A., Devanbu, P., & Filkov, V. (2014) · Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering · doi:10.1145/2635868.2635914 · Cited by 27
DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs
Kim, J., Lee, H., Nguyen, D. M., Shin, M., Kwon, B. C., Ko, S., & Elmqvist, N. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456340 · Cited by 7
Metadata
- Authors
- Palla, G., Barabási, A.-L., & Vicsek, T.
- Venue
- Nature
- Published
- 2007-04
- Volume
- 446
- Issue
- 7136
- Pages
- 664-667
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2092124750
- Cited by
- 1,668
- References
- 4
- FWCI
- 66.69
- 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.
