A mixture model approach for clustering bipartite networks
Applications
2019-07-19 v2 Methodology
Abstract
This chapter investigates the latent structure of bipartite networks via a model-based clustering approach which is able to capture both latent groups of sending nodes and latent variability of the propensity of sending nodes to create links with receiving nodes within each group. This modelling approach is very flexible and can be estimated by using fast inferential approaches such as variational inference. We apply this model to the analysis of a terrorist network in order to identify the main latent groups of terrorists and their latent trait scores based on their attendance to some events.
Cite
@article{arxiv.1905.02659,
title = {A mixture model approach for clustering bipartite networks},
author = {Isabella Gollini},
journal= {arXiv preprint arXiv:1905.02659},
year = {2019}
}
Comments
To appear in "Challenges in Social Network Research" Volume in the Lecture Notes in Social Networks (LNSN - Series of Springer)