ArticleScientific reports2013
Surprise maximization reveals the community structure of 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 21 papers.
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Who cites it
21 citing papers in PubMed.
- An investigative network analysis mapping global cancer epidemiology.Theory in biosciences = Theorie in den Biowissenschaften · 2026Article
- Estimating the risk and spatial spread of measles in populations with high MMR uptake: Using school-household networks to understand the 2013 to 2014 outbreak in the Netherlands.PLoS medicine · 2024Article
- Inflammatory bowel disease biomarkers revealed by the human gut microbiome network.Scientific reports · 2023Article
- Robust, scalable, and informative clustering for diverse biological networks.Genome biology · 2023Article
- Article
- Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements.AIMS neuroscience · 2021Article
- Identifying Influencers in Social Networks.Entropy (Basel, Switzerland) · 2020Article
- Structural Entropy: Monitoring Correlation-Based Networks Over Time With Application To Financial Markets.Scientific reports · 2019Article
- Critical analysis of (Quasi-)Surprise for community detection in complex networks.Scientific reports · 2018Article
- Article
- 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
- Identification of leader and self-organizing communities in complex networks.Scientific reports · 2017Article
- Community detection in weighted brain connectivity networks beyond the resolution limit.NeuroImage · 2017Article
- Article
- Multi-resolution community detection in massive networks.Scientific reports · 2016Article
- Modular structure of brain functional networks: breaking the resolution limit by Surprise.Scientific reports · 2016Article
- Identification of hybrid node and link communities in complex networks.Scientific reports · 2015Article
- Significant scales in community structure.Scientific reports · 2013Article
- Exploring the limits of community detection strategies in complex networks.Scientific reports · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
How to determine the community structure of complex networks is an open question. It is critical to establish the best strategies for community detection in networks of unknown structure. Here, using standard synthetic benchmarks, we show that none of the algorithms hitherto developed for community structure characterization perform optimally. Significantly, evaluating the results according to their modularity, the most popular measure of the quality of a partition, systematically provides mistaken solutions. However, a novel quality function, called Surprise, can be used to elucidate which is the optimal division into communities. Consequently, we show that the best strategy to find the community structure of all the networks examined involves choosing among the solutions provided by multiple algorithms the one with the highest Surprise value. We conclude that Surprise maximization precisely reveals the community structure of complex networks.
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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.