Abstract

Identification of network linkages through direct observation of human interaction has long been a staple of network analysis. It is, however, time consuming and labor intensive when undertaken by human observers. This paper describes the development and validation of a two-stage methodology for automating the identification of network links from direct observation of groups in which members are free to move around a space. The initial manual annotation stage utilizes a web-based interface to support manual coding of physical location, posture, and gaze direction of group members from snapshots taken from video recordings of groups. The second stage uses the manually annotated data as input for machine learning to automate the inference of links among group members. The manual codings were treated as observed variables and the theory of turn taking in conversation was used to model temporal dependencies among interaction links, forming a Dynamic Bayesian Network (DBN). The DBN was modeled using the Bayes Net Toolkit and parameters were learned using Expectation Maximization (EM) algorithm. The Viterbi algorithm was adapted to perform the inference in DBN. The result is a time series of linkages for arbitrarily long segments that utilizes statistical distributions to estimate linkages. The validity of the method was assessed through comparing the accuracy of automatically detected links to manually identified links. Results show adequate validity and suggest routes for improvement of the method.

Original languageEnglish (US)
Pages (from-to)515-526
Number of pages12
JournalSocial Networks
Volume34
Issue number4
DOIs
StatePublished - Oct 1 2012

Keywords

  • Collaborative groups
  • Dynamic Bayesian Network
  • Social networks

ASJC Scopus subject areas

  • Anthropology
  • Sociology and Political Science
  • Social Sciences(all)
  • Psychology(all)

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