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Bayesian Approach to Network Modularity

Andrew Mugler; Chris H. Wiggins

Title:
Bayesian Approach to Network Modularity
Author(s):
Mugler, Andrew
Wiggins, Chris H.
Date:
Type:
Articles
Department(s):
Applied Physics and Applied Mathematics
Volume:
100
Persistent URL:
Book/Journal Title:
Physical Review Letters
Publisher:
American Physical Society
Publisher Location:
New York
Abstract:
We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network. We show how several existing methods for finding modules can be described as variant, special, or limiting cases of our work, and how the method overcomes the resolution limit problem, accurately recovering the true number of modules. Our approach is based on Bayesian methods for model selection which have been used with success for almost a century, implemented using a variational technique developed only in the past decade. We apply the technique to synthetic and real networks and outline how the method naturally allows selection among competing models.
Subject(s):
Biophysics
Mathematics
Publisher DOI:
https://doi.org/10.1103/PhysRevLett.100.258701
Item views
178
Metadata:
text | xml
Suggested Citation:
Andrew Mugler, Chris H. Wiggins, , Bayesian Approach to Network Modularity, Columbia University Academic Commons, .

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