Modeling Node Exposure for Community Detection in Networks

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.

arXiv comment13 pages, 4 figures

References in corpus (13)

• Community structure in social and biological networks
• Community detection in graphs
• Link Prediction in Complex Networks: A Survey
• Community detection in networks: A user guide
• An efficient and principled method for detecting communities in networks
• Structure and inference in annotated networks
• Consistency of community detection in networks under degree-corrected stochastic block models
• Community detection, link prediction, and layer interdependence in multilayer networks
• Community detection with node attributes in multilayer networks
• Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations
• Reciprocity, community detection, and link prediction in dynamic networks
• Generative model for reciprocity and community detection in networks
• Community detection and reciprocity in networks by jointly modeling pairs of edges