Resolution limit in community detection
Open accessAbstract
Detecting community structure is fundamental for uncovering the links between structure and function in complex networks and for practical applications in many disciplines such as biology and sociology. A popular method now widely used relies on the optimization of a quantity called modularity, which is a quality index for a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a scale which depends on the total size of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined. This finding is confirmed through several examples, both in artificial and in real social, biological, and technological networks, where we show that modularity optimization indeed does not resolve a large number of modules. A check of the modules obtained through modularity optimization is thus necessary, and we provide here key elements for the assessment of the reliability of this community detection method.
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Modularity-based graph clustering suffers a well-known resolution limit, failing to find small clusters even when they are perfectly unambiguously defined.
“However, modularity-based graph clustering suffers a well-known resolution limit (Fortunato and Barthélemy, 2007), failing to find small clusters even when they are perfectly unambiguously defined.”
From Molecular Architecture of the Mouse Nervous System (2018) · cites this paper as Fortunato and Barthélemy, 2007
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- Authors
- Fortunato, S., & Barthélemy, M.
- Affiliations
- Institute for Scientific Interchange (IT)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Corresponding author
- Barthélemy, Marc
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2007-01-02
- Volume
- 104
- Issue
- 1
- Pages
- 36-41
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC1765466
- OpenAlex
- W2128366083
- Cited by
- 3,024
- References
- 29
- Citations by year
- 2026: 93 · 2025: 134 · 2024: 126 · 2023: 112 · 2022: 150 · 2021: 154 · 2020: 186 · 2019: 209 · 2018: 241 · 2017: 195 · 2016: 198 · 2015: 195 · 2014: 215 · 2013: 193 · 2012: 165
- FWCI
- 40.39
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- community detection, resolution limit, modularity optimization, network partitioning, complex networks, module reliability, social networks, biological networks, technological networks
- MeSH terms
- Animals, Humans, Models, Theoretical, Community Networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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