The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications
Open accessAbstract
In this paper we extend our previous work on the stochastic block model, a commonly used generative model for social and biological networks, and the problem of inferring functional groups or communities from the topology of the network. We use the cavity method of statistical physics to obtain an asymptotically exact analysis of the phase diagram. We describe in detail properties of the detectability-undetectability phase transition and the easy-hard phase transition for the community detection problem. Our analysis translates naturally into a belief propagation algorithm for inferring the group memberships of the nodes in an optimal way, i.e., that maximizes the overlap with the underlying group memberships, and learning the underlying parameters of the block model. Finally, we apply the algorithm to two examples of real-world networks and discuss its performance.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
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
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Spin Glass Theory and Beyond: An Introduction to the Replica Method and Its Applications
Mezard, M., Parisi, G., & Virasoro, M. (1986) · World Scientific Lecture Notes in Physics · doi:10.1142/0271 · Cited by 3,653
Hierarchical structure and the prediction of missing links in networks
Clauset, A., Moore, C., & Newman, M. E. J. (2008) · Nature · doi:10.1038/nature06830 · Cited by 2,171
Self-citationPerformance 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
Cited by
Newman, M. (2018) · doi:10.1093/oso/9780198805090.001.0001 · Cited by 2,297
Spectral redemption in clustering sparse networks
Krzakala, F., Moore, C., Mossel, E., Neeman, J., Sly, A., Zdeborová, L., & Zhang, P. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1312486110 · Cited by 638
Self-citationCommunity Detection and Stochastic Block Models
Abbe, E. (2018) · now publishers, Inc. eBooks · doi:10.1561/9781680834772 · Cited by 544
Statistical physics of inference: thresholds and algorithms
Zdeborová, L., & Krzakala, F. (2016) · Advances in Physics · doi:10.1080/00018732.2016.1211393 · Cited by 440
Self-citationConvolutional Neural Network Architectures for Signals Supported on Graphs
Gama, F., Marques, A. G., Leus, G., & Ribeiro, A. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2887403 · Cited by 308
Metadata
- Authors
- Decelle, A., Krzakala, F., Moore, C., & Zdeborová, L.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2011-12-12
- Volume
- 84
- Issue
- 6
- Pages
- 066106-066106
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W2004531067
- Cited by
- 747
- References
- 6
- FWCI
- 17.18
- Type
- journal article
- Language
- en
- Open access
- green
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