Matched bipartite block model with covariates
Social and Information Networks
2017-03-16 v1 Machine Learning
Machine Learning
Abstract
Community detection or clustering is a fundamental task in the analysis of network data. Many real networks have a bipartite structure which makes community detection challenging. In this paper, we consider a model which allows for matched communities in the bipartite setting, in addition to node covariates with information about the matching. We derive a simple fast algorithm for fitting the model based on variational inference ideas and show its effectiveness on both simulated and real data. A variation of the model to allow for degree-correction is also considered, in addition to a novel approach to fitting such degree-corrected models.
Cite
@article{arxiv.1703.04943,
title = {Matched bipartite block model with covariates},
author = {Zahra S. Razaee and Arash A. Amini and Jingyi Jessica Li},
journal= {arXiv preprint arXiv:1703.04943},
year = {2017}
}