Estimating the Number of Communities in a Network. Newman, M. E J & Reinert, G.
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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 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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