ArticlePloS one2011
Deciphering network community structure by surprise.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Complex networks applied to political analysis: Group voting behavior in the Brazilian congress.PloS one · 2025Article
- Efficient identification of multiple pathways: RNA-Seq analysis of livers fromBMC bioinformatics · 2020Article
- The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability.NeuroImage · 2019Article
- Global vs local modularity for network community detection.PloS one · 2018Article
- Inverse Resolution Limit of Partition Density and Detecting Overlapping Communities by Link-Surprise.Scientific reports · 2017Article
- A semi-synchronous label propagation algorithm with constraints for community detection in complex networks.Scientific reports · 2017Article
- Community detection in weighted brain connectivity networks beyond the resolution limit.NeuroImage · 2017Article
- Modular structure of brain functional networks: breaking the resolution limit by Surprise.Scientific reports · 2016Article
- Article
- Modular Brain Networks.Annual review of psychology · 2016Review
- Article
- Identifying robust communities and multi-community nodes by combining top-down and bottom-up approaches to clustering.Scientific reports · 2015Article
- Community Detection in Signed Networks: the Role of Negative ties in Different Scales.Scientific reports · 2015Article
- Article
- Detecting communities based on network topology.Scientific reports · 2014Article
- Online community detection for large complex networks.PloS one · 2014Article
- Significant scales in community structure.Scientific reports · 2013Article
- Exploring the limits of community detection strategies in complex networks.Scientific reports · 2013Article
- Improved network community structure improves function prediction.Scientific reports · 2013Article
- Surprise maximization reveals the community structure of complex networks.Scientific reports · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The analysis of complex networks permeates all sciences, from biology to sociology. A fundamental, unsolved problem is how to characterize the community structure of a network. Here, using both standard and novel benchmarks, we show that maximization of a simple global parameter, which we call Surprise (S), leads to a very efficient characterization of the community structure of complex synthetic networks. Particularly, S qualitatively outperforms the most commonly used criterion to define communities, Newman and Girvan's modularity (Q). Applying S maximization to real networks often provides natural, well-supported partitions, but also sometimes counterintuitive solutions that expose the limitations of our previous knowledge. These results indicate that it is possible to define an effective global criterion for community structure and open new routes for the understanding of complex networks.
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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.