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Cited by 673

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Statistical mechanics of networks

Open access
Park, Juyong, Newman, M. E. J.·2004·Physical Review E

Abstract

We study the family of network models derived by requiring the expected properties of a graph ensemble to match a given set of measurements of a real-world network, while maximizing the entropy of the ensemble. Models of this type play the same role in the study of networks as is played by the Boltzmann distribution in classical statistical mechanics; they offer the best prediction of network properties subject to the constraints imposed by a given set of observations. We give exact solutions of models within this class that incorporate arbitrary degree distributions and arbitrary but independent edge probabilities. We also discuss some more complex examples with correlated edges that can be solved approximately or exactly by adapting various familiar methods, including mean-field theory, perturbation theory, and saddle-point expansions.

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Metadata

Authors
Park, J., & Newman, M. E. J.
Venue
Physical Review E
Published
2004-12-07
Volume
70
Issue
6
Pages
066117-066117
ISSN
  • 1539-3755 (ISSN-L)
  • 1550-2376
OpenAlex
W1761171396
Cited by
673
References
4
FWCI
11.14
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Statistical mechanics of networks. https://app.cochat.ai/papers/10.1103/physreve.70.066117
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Park, J., & Newman, M. E. J. (2004). Statistical mechanics of networks. Physical Review E. https://doi.org/10.1103/physreve.70.066117

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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.