Minimum entropy stochastic block models neglect edge distribution heterogeneity

TL;DR

This paper critiques the use of minimum entropy stochastic block models, showing they can overlook edge distribution heterogeneity and fail to identify significant communities despite higher likelihood.

Contribution

It reveals limitations of minimum entropy models in community detection by highlighting their neglect of edge distribution heterogeneity.

Findings

Minimum entropy models may not capture edge distribution heterogeneity.

Such models can generate higher likelihood graphs but miss significant community structures.

The paper challenges the assumption that minimum entropy always yields the best community detection.

Abstract

The statistical inference of stochastic block models as emerged as a mathematicaly principled method for identifying communities inside networks. Its objective is to find the node partition and the block-to-block adjacency matrix of maximum likelihood i.e. the one which has most probably generated the observed network. In practice, in the so-called microcanonical ensemble, it is frequently assumed that when comparing two models which have the same number and sizes of communities, the best one is the one of minimum entropy i.e. the one which can generate the less different networks. In this paper, we show that there are situations in which the minimum entropy model does not identify the most significant communities in terms of edge distribution, even though it generates the observed graph with a higher probability.