Random effects in dynamic network actor models
Bibliographic Data
| ID | 6161643 |
|---|---|
| Authors | Alvaro Uzaheta (0000-0003-4367-3670, ETH Zurich, corresponding author), Amati (0000-0003-1190-1237, University of Milano-Bicocca), C Stadtfeld (0000-0002-2704-2134, ETH Zurich) |
| Year | 2023 |
| Volume | 11 |
| Issue | 2 |
| Pages | 249-266 |
| Publication date | 2023-02-06 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Network Science (JOURNAL) |
| Journal identifiers | ISSN: 2050-1250 • E-ISSN: 2050-1242 |
| Publisher | Cambridge University Press (PUBLISHER • US) |
| DOI | 10.1017/nws.2022.37 |
| OpenAlex | W4319319726 |
| Language | EN |
| Citations received | 3 |
| References cited | 18 |
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
Constant (computer programming · Decision process · Econometrics · Event (particle physics · Exponential random graph models · Machine learning · Management science · Mathematical economics · Multinomial distribution · Multinomial logistic regression · Outcome (game theory · Random graph · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Social Media and Politics · Artificial Intelligence · Theoretical Computer Science
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| Citations per year | 1.5 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |