title: The maximum likelihood method for detecting communities in communication networks
metadata.name: dc.contributor.author
metadata.value: Mazalov, Vladimir V.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Nikitina, Natalia N.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2018-09-14T10:59:10Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2018-09-14T10:59:10Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2018-09
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Mazalov V. V., Nikitina N. N. The maximum likelihood method for detecting communities in communication networks. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2018, vol. 14, iss. 3, pp. 200–214.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/11702/spbu10.2018.302
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/14824
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The community detection in social and communication networks is an important problem in
  many applied fields: biology, sociology, social networks. This is especially true for networks
  that are represented by large graphs. In this paper, we propose a method for community
  detection based on the maximum likelihood method for the random formation of a network
  with given parameters of the tightness of connections within the community and between
  different communities. A numerical algorithm for finding the maximum of the objective
  function over all possible network partitions is described. The algorithm is implemented and
  tested on real networks of small dimension.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: Работа выполнена при финансовой поддержке Российского научного фонда (проект №17-11-01079).
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 14; Issue 3
metadata.lang: -
metadata.name: dc.subject
metadata.value: network communities
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: detecting communities in a network
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: maximum likelihood method
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: Gibbs sampling
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: The maximum likelihood method for detecting communities in communication networks
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 02-Mazalov.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/14824/1/02-Mazalov.pdf
resources.format: Adobe PDF
resources.size: 579,59 kB