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Analysis of weighted networks
Open accessAbstract
The connections in many networks are not merely binary entities, either present or not, but have associated weights that record their strengths relative to one another. Recent studies of networks have, by and large, steered clear of such weighted networks, which are often perceived as being harder to analyze than their unweighted counterparts. Here we point out that weighted networks can in many cases be analyzed using a simple mapping from a weighted network to an unweighted multigraph, allowing us to apply standard techniques for unweighted graphs to weighted ones as well. We give a number of examples of the method, including an algorithm for detecting community structure in weighted networks and a simple proof of the maximum-flow-minimum-cut theorem.
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Cites
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
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
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
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Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
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BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010
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Clauset, A., Newman, M. E. J., & Moore, C. (2004) · Physical Review E · doi:10.1103/physreve.70.066111 · Cited by 7,390
Self-citationKivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014) · Journal of Complex Networks · doi:10.1093/comnet/cnu016 · Cited by 3,235
Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098679 · Cited by 2,909
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Roberts, J. A., Perry, A., Lord, A. R., Roberts, G., Mitchell, P. B., Smith, R. E., Calamante, F., & Breakspear, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.009 · Cited by 213
A formal analysis of cytokine networks in chronic fatigue syndrome
Broderick, G., Fuite, J., Kreitz, A., Vernon, S. D., Klimas, N., & Fletcher, M. A. (2010) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2010.04.012 · Cited by 202
Estimating psychological networks and their accuracy: A tutorial paper
Epskamp, S., Borsboom, D., & Fried, E. I. (2018) · Behavior Research Methods · doi:10.3758/s13428-017-0862-1 · Cited by 5,715
Miskovic, V., Ma, X., Chou, C.-A., Fan, M., Owens, M., Sayama, H., & Gibb, B. E. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.013 · Cited by 175
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Godwin, D., Barry, R. L., & Marois, R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1414466112 · Cited by 145
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Chen, Z., Liu, M., Gross, D. W., & Beaulieu, C. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00716 · Cited by 77
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Levé, M., Sueur, C., Petit, O., Matsuzawa, T., & Hirata, S. (2016) · Primates · doi:10.1007/s10329-015-0494-y · Cited by 41
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Trinkle, S., Foxley, S., Wildenberg, G., Kasthuri, N., & La Rivière, P. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118576 · Cited by 8
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Metadata
- Authors
- Newman, M. E. J.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2004-11-24
- Volume
- 70
- Issue
- 5
- Pages
- 056131-056131
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W1983345514
- Cited by
- 2,476
- References
- 4
- FWCI
- 19.79
- Type
- journal article
- Language
- en
- Open access
- green
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