doi abstract bibtex

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 powerful and flexible 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 using a maximum-likelihood method. We demonstrate this approach on a range of real-world examples with known community structure, finding that it is able to determine the number of communities correctly in every case.

@article{newmanEstimatingNumberCommunities2016, title = {Estimating the {{Number}} of {{Communities}} in a {{Network}}}, volume = {117}, issn = {10797114}, doi = {10.1103/PhysRevLett.117.078301}, 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 powerful and flexible 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 using a maximum-likelihood method. We demonstrate this approach on a range of real-world examples with known community structure, finding that it is able to determine the number of communities correctly in every case.}, number = {7}, journaltitle = {Physical Review Letters}, date = {2016}, author = {Newman, M. E J and Reinert, Gesine}, file = {/home/dimitri/Nextcloud/Zotero/storage/H3AZ34BL/Newman, Reinert - 2016 - Estimating the Number of Communities in a Network.pdf}, eprinttype = {pmid}, eprint = {27564002} }

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