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Modeling community detection using slow mixing random walks | IEEE Conference Publication | IEEE Xplore

Modeling community detection using slow mixing random walks


Abstract:

The task of community detection in a graph formalizes the intuitive task of grouping together subsets of vertices such that vertices within clusters are connected tighter...Show More

Abstract:

The task of community detection in a graph formalizes the intuitive task of grouping together subsets of vertices such that vertices within clusters are connected tighter than those in disparate clusters. This paper approaches community detection in graphs by constructing Markov random walks on the graphs. The mixing properties of the random walk are then used to identify communities. We use coupling from the past as an algorithmic primitive to translate the mixing properties of the walk into revealing the community structure of the graph. We analyze the performance of our algorithms on specific graph structures, including the stochastic block models (SBM) and LFR random graphs.
Date of Conference: 29 October 2015 - 01 November 2015
Date Added to IEEE Xplore: 28 December 2015
ISBN Information:
Conference Location: Santa Clara, CA, USA

References

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