Modeling the dynamics of social networks and continuous actor attributes
24 mins 32 secs,
357.02 MB,
MPEG-4 Video
640x360,
29.97 fps,
44100 Hz,
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| Description: |
Niezink, N (University of Groningen)
Thursday 25th August 2016 - 16:00 to 16:20 |
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| Created: | 2016-08-31 17:19 |
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| Collection: | Theoretical Foundations for Statistical Network Analysis |
| Publisher: | Isaac Newton Institute |
| Copyright: | Niezink, N |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Co-authors: Tom Snijders (University of Groningen)
Social networks and the characteristics of the actors who constitute these networks are not static; they evolve interdependently over time. People may befriend others with similar political opinions or change their own opinion based on that of their friends. The stochastic actor-oriented model is used to statistically model such dynamics. We will present an extension of this model for continuous dynamic actor characteristics. The method available until now assumed actor characteristics to be measured on an ordinal categorical scale, which yielded practical limitations for applied researchers. We now model the interdependent dynamics by a stochastic differential equation for the attribute evolution and a Markov chain model for the network evolution. Although the model is too complicated to calculate likelihoods or estimators in closed form, the stochastic evolution process can be easily simulated. Therefore, we estimate model parameters using the method of moments and the Robbins-Monro algorithm for stochastic approximation. We will illustrate the proposed method by a study of the relation between friendship and obesity, analyzing body mass index as continuous dynamic actor attribute. |
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