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Modeling the evolution of interaction behavior in social networks: A dynamic relational event approach for real-time analysis

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  • Mulder, Joris
  • Leenders, Roger Th.A.J.

Abstract

There has been an increasing interest in understanding how social networks evolve over time. The study of network dynamics is often based on modeling the transition of a (small) number of snapshots of the network observations. The approach however is not suitable for analyzing networks of event streams where edges are constantly changing in frequency, strength, sentiment, or type in real time.

Suggested Citation

  • Mulder, Joris & Leenders, Roger Th.A.J., 2019. "Modeling the evolution of interaction behavior in social networks: A dynamic relational event approach for real-time analysis," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 73-85.
    • Handle: RePEc:eee:chsofr:v:119:y:2019:i:c:p:73-85
    DOI: 10.1016/j.chaos.2018.11.027
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    References listed on IDEAS

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    Cited by:

    1. Fabio Vieira & Roger Leenders & Joris Mulder, 2024. "Fast meta-analytic approximations for relational event models: applications to data streams and multilevel data," Journal of Computational Social Science, Springer, vol. 7(2), pages 1823-1859, October.

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