Dynamic Network Actor Models (DyNAMs) assume that an observed sequence of relational events is the outcome of an actor-oriented decision process consisting of two decision levels. The first level represents the time until an actor initiates the next relational event, modeled by an exponential distribution with an actor-specific activity rate. The second level describes the choice of the receiver of the event, modeled by a conditional multinomial logit model. The DyNAM assumes that the parameters are constant over the actors and the context. This homogeneity assumption, albeit statistically and computationally convenient, is difficult to justify, e.g., in the presence of unobserved differences between actors or contexts. In this paper, we extend DyNAMs by including random-effects parameters that vary across actors or contexts and allow controlling for unknown sources of heterogeneity. We illustrate the model by analyzing relational events among the users of an online community of aspiring and professional digital and graphic designers.

Uzaheta, A., Amati, V., Stadtfeld, C. (2023). Random effects in dynamic network actor models. NETWORK SCIENCE, 11(S2), 249-266 [10.1017/nws.2022.37].

Random effects in dynamic network actor models

Amati, V;
2023

Abstract

Dynamic Network Actor Models (DyNAMs) assume that an observed sequence of relational events is the outcome of an actor-oriented decision process consisting of two decision levels. The first level represents the time until an actor initiates the next relational event, modeled by an exponential distribution with an actor-specific activity rate. The second level describes the choice of the receiver of the event, modeled by a conditional multinomial logit model. The DyNAM assumes that the parameters are constant over the actors and the context. This homogeneity assumption, albeit statistically and computationally convenient, is difficult to justify, e.g., in the presence of unobserved differences between actors or contexts. In this paper, we extend DyNAMs by including random-effects parameters that vary across actors or contexts and allow controlling for unknown sources of heterogeneity. We illustrate the model by analyzing relational events among the users of an online community of aspiring and professional digital and graphic designers.
Articolo in rivista - Articolo scientifico
Bayesian estimation; Dynamic Network Actor Models; heterogeneity; random-effects models; relational event data;
English
6-feb-2023
2023
11
S2
249
266
open
Uzaheta, A., Amati, V., Stadtfeld, C. (2023). Random effects in dynamic network actor models. NETWORK SCIENCE, 11(S2), 249-266 [10.1017/nws.2022.37].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/417041
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