The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Mean-field theory for scale-free random networks
Open accessAbstract
Random networks with complex topology are common in Nature, describing systems as diverse as the world wide web or social and business networks. Recently, it has been demonstrated that most large networks for which topological information is available display scale-free features. Here we study the scaling properties of the recently introduced scale-free model, that can account for the observed power-law distribution of the connectivities. We develop a mean-field method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the scaling exponents. The mean-field method can be used to address the properties of two variants of the scale-free model, that do not display power-law scaling.
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Citations
Cites
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
Cited by
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Self-citationStatistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
Self-citationThe Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Complex networks: structure and dynamics
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
Error and attack tolerance of complex networks
Albert, R., Jeong, H., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35019019 · Cited by 7,534
“The inhomogeneous connectivity distribution of many real networks is reproduced by the scale-free model [17,18] that incorporates two ingredients common to real networks: growth and preferential attachment.”
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Metadata
- Authors
- Barabási, A.-L., Albert, R., & Jeong, H.
- Venue
- Physica A: Statistical Mechanics and its Applications
- Publisher
- Elsevier BV
- Published
- 1999-10
- Volume
- 272
- Issue
- 1-2
- Pages
- 173-187
- ISSN
- 0378-4371 (ISSN-L)
- OpenAlex
- W2121821841
- Cited by
- 2,255
- References
- 1
- FWCI
- 27.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Barabási, A.-L., Albert, R., & Jeong, H. (1999). Mean-field theory for scale-free random networks. Physica A: Statistical Mechanics and its Applications. https://doi.org/10.1016/s0378-4371(99)00291-5
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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.
