title: Estimating the Number of Communities in a Network.
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.
authors: Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.|Rudolph Peierls Centre for Theoretical Physics, University of Oxford, 1 Keble Road, Oxford OX1 3NP, United Kingdom.
authors: M E J
authors: ME
authors: Newman
authors: Department of Statistics, University of Oxford, 24-29 St. Giles, Oxford OX1 3LB, United Kingdom.
authors: Gesine
authors: G
authors: Reinert