The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Uncovering space-independent communities in spatial networks
Open accessAbstract
Many complex systems are organized in the form of a network embedded in space. Important examples include the physical Internet infrastucture, road networks, flight connections, brain functional networks, and social networks. The effect of space on network topology has recently come under the spotlight because of the emergence of pervasive technologies based on geolocalization, which constantly fill databases with people’s movements and thus reveal their trajectories and spatial behavior. Extracting patterns and regularities from the resulting massive amount of human mobility data requires the development of appropriate tools for uncovering information in spatially embedded networks. In contrast with most works that tend to apply standard network metrics to any type of network, we argue in this paper for a careful treatment of the constraints imposed by space on network topology. In particular, we focus on the problem of community detection and propose a modularity function adapted to spatial networks. We show that it is possible to factor out the effect of space in order to reveal more clearly hidden structural similarities between the nodes. Methods are tested on a large mobile phone network and computer-generated benchmarks where the effect of space has been incorporated.
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Cites
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
The norm of reciprocity: A preliminary statement
Gouldner, A. W. (1960) · American Sociological Review · doi:10.2307/2092623 · Cited by 12,001
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Functional cartography of complex metabolic networks
Guimerà, R., & Nunes Amaral, L. A. (2005) · Nature · doi:10.1038/nature03288 · Cited by 3,983 · cited 1×
“Community detection is a central tool of network theory because revealing intermediate scales of network organization provides the means to draw readable maps of the network and to uncover hidden functional relations between nodes (32).”
BackgroundLimits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234
Classes of small-world networks
Amaral, L. A. N., Scala, A., Barthélémy, M., & Stanley, H. E. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.200327197 · 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
Modular and hierarchically modular organization of brain networks
Meunier, D., Lambiotte, R., & Bullmore, E. T. (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00200 · Cited by 1,156
Self-citation
Cited by
Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., & Portugali, Y. (2012) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2012-01703-3 · Cited by 2,424
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
Sporns, O., & Betzel, R. F. (2016) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033634 · Cited by 1,334
Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data
Liu, Y., Sui, Z., Kang, C., & Gao, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086026 · Cited by 393
Structure and inference in annotated networks
Newman, M. E. J., & Clauset, A. (2016) · Nature Communications · doi:10.1038/ncomms11863 · Cited by 379
Metadata
- Authors
- Expert, P., Evans, T. S., Blondel, V. D., & Lambiotte, R.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2011-04-25
- Volume
- 108
- Issue
- 19
- Pages
- 7663-7668
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2075557508
- Cited by
- 350
- References
- 14
- FWCI
- 21.16
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Expert, P., Evans, T. S., Blondel, V. D., & Lambiotte, R. (2011). Uncovering space-independent communities in spatial networks. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1018962108
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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.
