Partitioning multimode networks

Martin G. Everett, Steve P. Borgatti

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

7 Scopus citations

Abstract

Most networks examined so far involve connections between nodes all of the same type, known as one-mode networks. This chapter examines partitioning and clustering in multimode network data. A number of techniques have been developed for dealing with non-binary data or more precisely non-network type data. The chapter first concentrates on two mode datasets and then discusses general multimode approaches. In considering two-mode networks the authors consider the problem of partitioning both modes to find sets of actors and events. For single-mode networks the most commonly used and accepted technique is Newman’s community detection, which optimizes modularity. M. J. Barber extended modularity to two-mode data and developed an algorithm specifically for this type of data.

Original languageEnglish
Title of host publicationAdvances in Network Clustering and Blockmodeling
Pages251-265
Number of pages15
ISBN (Electronic)9781119483298
DOIs
StatePublished - Dec 13 2019

Bibliographical note

Publisher Copyright:
© 2020 John Wiley & Sons Ltd.

Keywords

  • Community detection
  • Complex data
  • Multimode network data
  • Network clustering
  • Signed two-mode networks
  • Spectral methods
  • Two-mode partitioning

ASJC Scopus subject areas

  • General Mathematics

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