Estimating the Number of Communities in a Network

M. E. J. Newman and Gesine Reinert
Phys. Rev. Lett. 117, 078301 – Published 11 August 2016

Abstract

Community detection, the division of a network into dense subnetworks with only sparse connections between them, has been a topic of vigorous study in recent years. However, while there exist a range of effective methods for dividing a network into a specified number of communities, it is an open question how to determine exactly how many communities one should use. Here we describe a mathematically principled approach for finding the number of communities in a network by maximizing the integrated likelihood of the observed network structure under an appropriate generative model. We demonstrate the approach on a range of benchmark networks, both real and computer generated.

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  • Received 16 May 2016

DOI:https://doi.org/10.1103/PhysRevLett.117.078301

© 2016 American Physical Society

Physics Subject Headings (PhySH)

Networks

Authors & Affiliations

M. E. J. Newman1,2 and Gesine Reinert3

  • 1Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA
  • 2Rudolph Peierls Centre for Theoretical Physics, University of Oxford, 1 Keble Road, Oxford OX1 3NP, United Kingdom
  • 3Department of Statistics, University of Oxford, 24-29 St. Giles, Oxford OX1 3LB, United Kingdom

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Issue

Vol. 117, Iss. 7 — 12 August 2016

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