Using Network Analysis for Examining Interpersonal Emotion Dynamics

Eran Bar-Kalifa, Haran Sened

Research output: Contribution to journalArticlepeer-review


Several contemporary models conceptualize emotion as inherently interpersonal. We demonstrate how network analysis, a class of statistical methods often used to examine intrapersonal dynamic processes, provides a potential avenue for parameterizing interpersonal emotion dynamics (and interpersonal dynamics in general). We claim that this method allows (a) observing interpersonal dynamics at various temporal levels; (b) examining interpersonal dynamics occurring through various emotional pathways; and (c) capturing variations in interpersonal networks, which can subsequently be used to predict changes in outcomes. To demonstrate the potential of this method, we used dyadic daily diary data on emotion dynamics from two samples; Sample 1 involved couples in their routine daily lives, whereas Sample 2 involved couples in their transition to parenthood. Graphical Multilevel-Vector-Autoregressive modeling was used to estimate partners’ emotional networks, whereas in a second step, LASSO was used to test the predictive value of couple-level differences of the obtained networks. The analysis revealed several patterns. For example, the between-couple network of Sample 1 was more interpersonally dense, but couple-level differences in the networks’ interpersonal associations were predictive of partners’ relationship satisfaction over time only in Sample 2. We also include commented code implementing a new dyadmlvar R package developed for conducting this analysis.

Original languageAmerican English
Pages (from-to)211-230
Number of pages20
JournalMultivariate Behavioral Research
Issue number2
StatePublished - 3 Mar 2020


  • Network analysis
  • emotion
  • graphical multi-level VAR
  • interpersonal emotion dynamics
  • relationships

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)
  • Statistics and Probability


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