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Literature DB >> 17025687

Community detection as an inference problem.

Abstract

We express community detection as an inference problem of determining the most likely arrangement of communities. We then apply belief propagation and mean-field theory to this problem, and show that this leads to fast, accurate algorithms for community detection.

Year: 2006 PMID： 17025687 DOI： 10.1103/PhysRevE.74.035102

Source DB: PubMed Journal: Phys Rev E Stat Nonlin Soft Matter Phys ISSN： 1539-3755

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Cited

9 in total

1. Mixture models and exploratory analysis in networks.

Authors: M E J Newman; E A Leicht
Journal: Proc Natl Acad Sci U S A Date: 2007-05-24 Impact factor: 11.205

2. Bayesian approach to network modularity.

Authors: Jake M Hofman; Chris H Wiggins
Journal: Phys Rev Lett Date: 2008-06-23 Impact factor: 9.161

3. Scalable detection of statistically significant communities and hierarchies, using message passing for modularity.

Authors: Pan Zhang; Cristopher Moore
Journal: Proc Natl Acad Sci U S A Date: 2014-12-08 Impact factor: 11.205

Review 4. Modular Brain Networks.

Authors: Olaf Sporns; Richard F Betzel
Journal: Annu Rev Psychol Date: 2015-09-21 Impact factor: 24.137

Community detection as an inference problem.

1. Mixture models and exploratory analysis in networks.

2. Bayesian approach to network modularity.

3. Scalable detection of statistically significant communities and hierarchies, using message passing for modularity.

Review 4. Modular Brain Networks.

5. The function of communities in protein interaction networks at multiple scales.

6. Resampling effects on significance analysis of network clustering and ranking.

7. Significant communities in large sparse networks.

8. Label propagation method based on bi-objective optimization for ambiguous community detection in large networks.

9. Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks.