Modeling Node Exposure for Community Detection in Networks

TL;DR

This paper introduces a Bayesian community detection method that explicitly models node exposure, improving graph reconstruction and enabling exposure probability estimation in networks with sampling bias.

Contribution

It presents a novel Bayesian framework incorporating hidden variables for exposure, enhancing community detection accuracy under sampling bias conditions.

Findings

Better graph reconstruction compared to non-exposure models

Maintains similar community recovery performance

Enables estimation of node exposure probabilities

Abstract

In community detection, datasets often suffer a sampling bias for which nodes which would normally have a high affinity appear to have zero affinity. This happens for example when two affine users of a social network were not exposed to one another. Community detection on this kind of data suffers then from considering affine nodes as not affine. To solve this problem, we explicitly model the (non-)exposure mechanism in a Bayesian community detection framework, by introducing a set of additional hidden variables. Compared to approaches which do not model exposure, our method is able to better reconstruct the input graph, while maintaining a similar performance in recovering communities. Importantly, it allows to estimate the probability that two nodes have been exposed, a possibility not available with standard models.