ArticleScientific reports2013
Exploring the limits of community detection strategies in complex networks.
Article in Scientific reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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Who cites it
15 citing papers in PubMed.
- Understanding the role of international flight networks in disease spread: spatial epidemic prevention zones and hierarchical disease control policies.International journal of health geographics · 2026Article
- Analysis Method for the Spatial Layout Equilibrium of Highway Transportation Network Based on Community Detection.Sensors (Basel, Switzerland) · 2025Article
- A general rule on the organization of biodiversity in Earth's biogeographical regions.Nature ecology & evolution · 2025Article
- Flexibility in the social structure of male chimpanzees (Royal Society open science · 2022Article
- From groups to communities in western lowland gorillas.Proceedings. Biological sciences · 2019Article
- Global vs local modularity for network community detection.PloS one · 2018Article
- Network-based analysis of diagnosis progression patterns using claims data.Scientific reports · 2017Article
- Infomap Bioregions: Interactive Mapping of Biogeographical Regions from Species Distributions.Systematic biology · 2017Article
- The modular organization of human anatomical brain networks: Accounting for the cost of wiring.Network neuroscience (Cambridge, Mass.) · 2017Article
- Article
- Community Detection in Signed Networks: the Role of Negative ties in Different Scales.Scientific reports · 2015Article
- Article
- Network structure and community evolution on Twitter: human behavior change in response to the 2011 Japanese earthquake and tsunami.Scientific reports · 2014Article
- Interaction patterns between potato virus Y and eIF4E-mediated recessive resistance in the Solanaceae.Journal of virology · 2014Article
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The characterization of network community structure has profound implications in several scientific areas. Therefore, testing the algorithms developed to establish the optimal division of a network into communities is a fundamental problem in the field. We performed here a highly detailed evaluation of community detection algorithms, which has two main novelties: 1) using complex closed benchmarks, which provide precise ways to assess whether the solutions generated by the algorithms are optimal; and, 2) A novel type of analysis, based on hierarchically clustering the solutions suggested by multiple community detection algorithms, which allows to easily visualize how different are those solutions. Surprise, a global parameter that evaluates the quality of a partition, confirms the power of these analyses. We show that none of the community detection algorithms tested provide consistently optimal results in all networks and that Surprise maximization, obtained by combining multiple algorithms, obtains quasi-optimal performances in these difficult benchmarks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.