Journal
PHYSICAL REVIEW LETTERS
Volume 117, Issue 7, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.117.078301
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Funding
- U.S. National Science Foundation [DMS-1107796, DMS-1407207]
- United Kingdom Engineering and Physical Sciences Research Council [EP/K032402/1]
- Advanced Studies Centre at Keble College, Oxford
- EPSRC [EP/K032402/1, EP/J013501/1] Funding Source: UKRI
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1407207] Funding Source: National Science Foundation
- Alan Turing Institute [TU/B/000051] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/J013501/1, EP/K032402/1] Funding Source: researchfish
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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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