Fast unfolding of communities in large networks
Open accessAbstract
We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.
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Both the Louvain and Leiden algorithms identify Kumārajīva and his students as a community distinct from Dao'an and Huiyuan.
“Both the Louvain (Blondel et al. 2008) and the Leiden algorithm (Traag, Waltman, and van Eck 2018) can identify Kumārajīva and his students as a community distinct from that of Dao'an and Huiyuan (fig.2-1).”
From On the Use of Historical Social Network Analysis in the Study of Chinese Buddhism: The Case of Dao’an, Huiyuan, and Kumārajīva (2020) · cites this paper as Blondel et al. 2008
Kalehbasti et al. proposed replacing the greedy heuristic within the Louvain algorithm by a QUBO solver to consider a larger search space in each iteration.
“Kalehbasti et al.[74] proposed the generalizable idea of replacing the greedy heuristic within the Louvain algorithm [165], a classical heuristic for the k-community detection problem, by a QUBO solver to consider a larger search space in each iteration [74].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [165]
Unsupervised clustering by Louvain algorithm and UMAP produced 38 distinct cell clusters.
“Unsupervised clustering by the Louvain algorithm60 and visualization by uniform manifold approximation and projection (UMAP) produced 38 distinct cell clusters”
From The APOE-R136S mutation protects against APOE4-driven tau pathology, neurodegeneration, and neuroinflammation (2023) · cites this paper as [60]
There has been an influx of algorithms that seek to improve upon the traditionally used community detection ones.
“Along with this growth there has been an influx of algorithms that seek to improve upon the traditionally used ones (e.g., [53]).”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [53]
After PhenoGraph, the standard approach to clustering single-cell datasets has become multi-resolution modularity optimization as implemented in the Louvain algorithm on single-cell KNN graphs.
“After the pioneering PhenoGraph method (Levine et al, 2015), the standard approach to clustering single‐cell datasets has become multi‐resolution modularity optimization (Newman & Girvan, 2004; Reichardt & Bornholdt, 2006) as implemented in the Louvain algorithm (Blondel et al, 2008) on single‐cell KNN graphs.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Blondel et al, 2008
Community detection algorithms such as Louvain or Newman-Girvan determine the approximate number of communities by optimizing modularity, or the density of links within communities versus links between communities.
“Community detection algorithms such as Louvain (Blondel et al., 2008) or Newman Girvan (Girvan and Newman, 2002) determine the approximate number of communities by optimizing modularity, or the density of links within communities versus links between communities.”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Blondel et al., 2008
Community detection in igraph includes Leiden, multilevel (or Louvain), and fluid communities.
“Community detection includes, among others, Leiden [ 26 ], multilevel (or Louvain) [ 30 ] and fluid communities [ 31 ].”
From igraph 1.0 enables fast and robust network analysis across programming languages (2026) · cites this paper as [30]
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- Authors
- Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E.
- Affiliations
- UCLouvain (BE)
- LIP6 (FR)
- Imperial College London (GB)
- Venue
- Journal of Statistical Mechanics: Theory and Experiment
- Publisher
- IOP Publishing
- Published
- 2008-10-01
- Volume
- 2008
- Issue
- 10
- Pages
- P10008
- ISSN
- 1742-5468 (ISSN-L)
- OpenAlex
- W2131681506
- Cited by
- 21,424
- References
- 3
- Citations by year
- 2026: 1670 · 2025: 1998 · 2024: 2038 · 2023: 1890 · 2022: 1955 · 2021: 2137 · 2020: 1821 · 2019: 1612 · 2018: 1411 · 2017: 1122 · 2016: 1014 · 2015: 837 · 2014: 716 · 2013: 526 · 2012: 314
- FWCI
- 90.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Statistical Mechanics Theory and Experiment (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Repository of the University of Namur (submittedVersion, other-oa)
- HAL (submittedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Agence Nationale de la Recherche
- Fédération Wallonie-Bruxelles
- 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, modularity optimization, large-scale networks, mobile phone networks, language communities, web graph, modular networks, computational efficiency
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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