Community detection in networks without observing edges

TL;DR

This paper introduces a Bayesian hierarchical model for community detection in networks where edges are unobserved, using node signals to infer communities with uncertainty propagation and multiscale capabilities.

Contribution

The authors develop a novel Bayesian approach that detects communities without direct edge observations, incorporating uncertainty and enabling multiscale analysis.

Findings

Effective in synthetic data tests

Successfully applied to financial data

Supports multiscale community detection

Abstract

We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals for each of the nodes. Fitting the model provides an end-to-end community detection algorithm that does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approach naturally supports multiscale community detection as well as the selection of an optimal scale using model comparison. We study the properties of the algorithm using synthetic data and apply it to daily returns of constituents of the S&P100 index as well as climate data from US cities.