Measuring knowledge and experience in two mode temporal networks

Martin G. Everett, Chiara Broccatelli, Stephen P. Borgatti, Johan Koskinen

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Two mode social network data consisting of actors attending events is a common type of social network data. For these kinds of data it is also common to have additional information about the timing or sequence of the events. We call data of this type two-mode temporal data. We explore the idea that actors attending events gain information from the event in two ways. Firstly the event itself may provide information or training; secondly, as co-attendees interact, they may pass on skills or information they have gleaned from other events. We propose a method of measuring these gains and demonstrate its usefulness using the classic Southern Women Data and a covert network dataset.

Original languageEnglish
Pages (from-to)63-73
Number of pages11
JournalSocial Networks
StatePublished - Oct 2018

Bibliographical note

Publisher Copyright:
© 2018 Elsevier B.V.


  • Covert networks
  • Experience
  • Knowledge
  • Temporal networks
  • Two-mode networks

ASJC Scopus subject areas

  • Anthropology
  • Sociology and Political Science
  • General Social Sciences
  • General Psychology


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