Mixed-membership of experts stochastic blockmodel
Bibliographic Data
| ID | 6161571 |
|---|---|
| Authors | Arthur White (0000-0002-7268-5163, Trinity College Dublin), Thomas Brendan Murphy (0000-0002-5668-7046, University College Dublin) |
| Year | 2015 |
| Volume | 4 |
| Issue | 1 |
| Pages | 48-80 |
| Publication date | 2015-12-16 |
| 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.2015.29 |
| OpenAlex | W1841028234 |
| Language | EN |
| Citations received | 4 |
| References cited | 44 |
Social network analysis is the study of how links between a set of actors are formed. Typically, it is believed that links are formed in a structured manner, which may be due to, for example, political or material incentives, and which often may not be directly observable. The stochastic blockmodel represents this structure using latent groups which exhibit different connective properties, so that conditional on the group membership of two actors, the probability of a link being formed between them is represented by a connectivity matrix. The mixed membership stochastic blockmodel extends this model to allow actors membership to different groups, depending on the interaction in question, providing further flexibility. Attribute information can also play an important role in explaining network formation. Network models which do not explicitly incorporate covariate information require the analyst to compare fitted network models to additional attributes in a post-hoc manner. We introduce the mixed membership of experts stochastic blockmodel, an extension to the mixed membership stochastic blockmodel which incorporates covariate actor information into the existing model. The method is illustrated with application to the Lazega Lawyers dataset. Model and variable selection methods are also discussed
Covariate · Data mining · Econometrics · Extension (predicate logic · Flexibility (engineering · Latent variable · Machine learning · Set (abstract data type · Social network analysis · Statistics · Complex Network Analysis Techniques · Computational and Text Analysis Methods · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Artificial Intelligence · Theoretical Computer Science
A mixed membership model-based measure for subgroup integration in social networks
A Statistical Model of Bipartite Networks
Estimating the effects of network covariates on subgroup insularity with a hierarchical mixed membership stochastic blockmodel
Substantive implications of unobserved heterogeneity
Social Network Analysis
Estimation and Prediction for Stochastic Blockstructures
Goodness of Fit of Social Network Models
On random graphs. I.
A solution to the problem of separation in logistic regression
Latent Space Approaches to Social Network Analysis
An Exponential Family of Probability Distributions for Directed Graphs
A correlated topic model of Science
Diversity and Evenness
The Statistical Analysis of Compositional Data
On Information and Sufficiency
Bayes Factors
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Stochastic blockmodels
Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models
Recent developments in exponential random graph (p*) models for social networks
New Specifications for Exponential Random Graph Models
| Citations per year | 0.44 |
|---|---|
| Citation span | 2017 - 2025 (9) |
| Highly cited | No |