A Bayesian Approach to Modeling Stochastic Blockstructures With Covariates
Bibliographic Data
| ID | 10142109 |
|---|---|
| Authors | Christian Tallberg (Stockholm University, corresponding author) |
| Year | 2004 |
| Volume | 29 |
| Issue | 1 |
| Pages | 1-23 |
| Publication date | 2004-12-30 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Mathematical Sociology (JOURNAL) |
| Journal identifiers | ISSN: 0022-250X • E-ISSN: 1545-5874 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/00222500590889703 |
| OpenAlex | W1986592861 |
| Language | EN |
| Citations received | 9 |
| References cited | 26 |
We consider social networks in which the relations between actors are governed by latent classes of actors with similar relational structure, i.e., blockmodeling. In Snijders and Nowicki (1997 Snijders , T. A. B. & Nowicki , K. ( 1997 ). Estimation and prediction for stochastic blockmodels for graphs with latent blockstrucutre . Journal of classification , 14 , 75 – 100 . [CROSSREF] [CSA] [Crossref], [Web of Science ®] , [Google Scholar]) and Nowicki and Snijders (2001 Nowicki , K. & Snijders , T. A. B. ( 2001 ). Estimation and prediction for stochastic blockstructures . Journal of the American Statistical Association , 96 , 1077 – 1087 . [CROSSREF] [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]), a Bayesian approach to blockmodels is presented, where the probability of a relation between two actors depends only on the classes to which the actors belong but is independent of the actors. When actors are a priori partitioned into subsets based on actor attributes such as race, sex and income, the model proposed by Nowicki and Snijders completely ignores this extra piece of information. In this paper, a blockmodel that is a simple extension of their model is proposed specifically for such data. The class affiliation probabilities are modeled conditional on the actor attributes via a multinomial probit model. Posterior distributions of the model parameters, and predictive posterior distributions of the class affiliation probabilities are computed by using a straightforward Gibbs sampling algorithm. Applications are illustrated with analysis on real and simulated data sets
Bayesian probability · Covariate · Econometrics · Gibbs sampling · Latent class model · Machine learning · Multinomial probit · Probit model · Artificial Intelligence · Complex Network Analysis Techniques · Computer Science · Consumer Market Behavior and Pricing · Mathematics · Opinion Dynamics and Social Influence
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| Citations per year | 0.47 |
|---|---|
| Citation span | 2007 - 2021 (15) |
| Highly cited | No |