Maps of random walks on complex networks reveal community structure
Open accessAbstract
To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network—including physics, chemistry, molecular biology, and medicine—information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.
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- Authors
- Rosvall, M., & Bergstrom, C. T.
- Affiliations
- University of Washington (US)
- Santa Fe Institute (US)
- Corresponding author
- Rosvall, Martin
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2008-01-29
- Volume
- 105
- Issue
- 4
- Pages
- 1118-1123
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2234100
- OpenAlex
- W2164998314
- Cited by
- 4,583
- References
- 4
- Citations by year
- 2026: 207 · 2025: 297 · 2024: 288 · 2023: 277 · 2022: 321 · 2021: 362 · 2020: 342 · 2019: 385 · 2018: 407 · 2017: 327 · 2016: 315 · 2015: 265 · 2014: 241 · 2013: 184 · 2012: 142
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institute of General Medical Sciences (T34GM007649, 5U01GM07649)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- community structure, random walks, complex networks, information flow, probability flow, network mapping, weighted networks, directed graphs, citation networks, science communication, journal citations, information theory, network decomposition
- MeSH terms
- Information Theory, Markov Chains, Periodicals as Topic, Science, Bibliometrics, Biomedical Research
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