Efficient method for estimating the number of communities in a network

Maria A. Riolo, George T. Cantwell, Gesine Reinert, and M. E. J. Newman
Phys. Rev. E 96, 032310 – Published 14 September 2017

Abstract

While there exist a wide range of effective methods for community detection in networks, most of them require one to know in advance how many communities one is looking for. Here we present a method for estimating the number of communities in a network using a combination of Bayesian inference with a novel prior and an efficient Monte Carlo sampling scheme. We test the method extensively on both real and computer-generated networks, showing that it performs accurately and consistently, even in cases where groups are widely varying in size or structure.

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  • Received 16 June 2017

DOI:https://doi.org/10.1103/PhysRevE.96.032310

©2017 American Physical Society

Physics Subject Headings (PhySH)

Networks

Authors & Affiliations

Maria A. Riolo1, George T. Cantwell2, Gesine Reinert3, and M. E. J. Newman1,2

  • 1Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan 48109, USA
  • 2Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA
  • 3Department of Statistics, University of Oxford, 24–29 St. Giles, Oxford OX1 3LB, United Kingdom

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Issue

Vol. 96, Iss. 3 — September 2017

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