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Nodal Heterogeneity can Induce Ghost Triadic Effects in Relational Event Models.

Rūta Juozaitienė1, Ernst C Wit2

  • 1Vytautas Magnus University, Kaunas, Lithuania. ruta.juozaitiene@vdu.lt.

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Summary
This summary is machine-generated.

Temporal network analysis can be improved by accounting for sender and receiver effects. A new random-effect model resolves ghost effects caused by unobserved node heterogeneity, enhancing relational event models.

Keywords:
expansivenesshierarchy principlepopularityrandom effectsrelational event modellingtriadic effects

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Area of Science:

  • Network Science
  • Computational Social Science
  • Statistical Modeling

Background:

  • Temporal network data captures interactions over time, like co-authorship or emails.
  • Relational event frameworks model temporal dependencies and network dynamics.
  • Understanding endogenous mechanisms (reciprocity, triadic effects) and actor attributes is crucial.

Purpose of the Study:

  • To address issues of nodal heterogeneity in temporal network analysis.
  • To demonstrate how unobserved sender and receiver effects can create spurious triadic effects.
  • To propose a novel random-effect extension for relational event models.

Main Methods:

  • Developed a random-effect extension of the relational event model.
  • Compared the proposed model against traditional approaches like in-degree and out-degree statistics.
  • Investigated the impact of unobserved nodal heterogeneity on network dynamics.

Main Results:

  • Failing to account for sender and receiver effects can induce 'ghost' triadic effects.
  • The proposed random-effect model effectively resolves issues caused by nodal heterogeneity.
  • The random-effect extension outperforms traditional methods in capturing network dynamics.

Conclusions:

  • Including random effects in relational event models is essential for accurately capturing network dynamics.
  • This approach resolves the violation of the hierarchy principle caused by insufficient information on nodal heterogeneity.
  • The proposed method offers a robust solution for analyzing complex temporal network data.