The DOI matches its Crossref record; 28 of the 29 references it cites were found in a registry or an index.
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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“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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Cites
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Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
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Cited by
Sorted by citations
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Self-citationFrom Louvain to Leiden: guaranteeing well-connected communities
Traag, V. A., Waltman, L., & van Eck, N. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-41695-z · Cited by 5,635
An Information Flow Model for Conflict and Fission in Small Groups
Zachary, W. W. (1977) · Journal of Anthropological Research · doi:10.1086/jar.33.4.3629752 · Cited by 4,709
Benchmark graphs for testing community detection algorithms
Lancichinetti, A., Fortunato, S., & Radicchi, F. (2008) · Physical Review E · doi:10.1103/physreve.78.046110 · Cited by 3,058
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Metadata
- 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
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