TL;DR
This paper introduces a novel stochastic block model for multilevel networks, capturing complex dependencies between individual and organizational interactions, with applications demonstrated in sociological and economic network analysis.
Contribution
It proposes a flexible probabilistic framework for multilevel networks that models heterogeneity and dependencies without predefined connection patterns.
Findings
The model effectively captures multilevel network structures.
The approach demonstrates robustness and reliable model selection.
Application reveals intertwined sociological and economic network patterns.
Abstract
A multilevel network is defined as the junction of two interaction networks, one level representing the interactions between individuals and the other the interactions between organizations. The levels are linked by an affiliation relationship, each individual belonging to a unique organization. A new Stochastic Block Model is proposed as a unified probalistic framework tailored for multilevel networks. This model contains latent blocks accounting for heterogeneity in the patterns of connection within each level and introducing dependencies between the levels. The sought connection patterns are not specified a priori which makes this approach flexible. Variational methods are used for the model inference and an Integrated Classified Likelihood criterion is developed for choosing the number of blocks and also for deciding whether the two levels are dependent or not. A comprehensive…
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