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A Method Based on Total Variation for Network Modularity Optimization using the MBO Scheme | | Huiyi Hu
; Thomas Laurent
; Mason A. Porter
; Andrea L. Bertozzi
; | | Date: |
17 Apr 2013 | | Abstract: | The study of network structure is pervasive in sociology, biology, computer
science, and many other disciplines. One of the most important areas of network
science is the algorithmic detection of cohesive groups of nodes called
"communities". One popular approach to find communities is to maximize a
quality function known as {em modularity} to achieve some sort of optimal
clustering of nodes. In this paper, we interpret the modularity function from a
novel perspective: we reformulate modularity optimization as a minimization
problem of an energy functional that consists of a total variation term and an
$ell_2$ balance term. By employing numerical techniques from image processing
and $ell_1$ compressive sensing -- such as convex splitting and the
Merriman-Bence-Osher (MBO) scheme -- we develop a variational algorithm for the
minimization problem. We present our computational results using both synthetic
benchmark networks and real data. | | Source: | arXiv, 1304.4679 | | Services: | Forum | Review | PDF | Favorites | |
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