Community detection in networks without observing edges
This addresses the challenge of identifying communities in networks with indirect edge observations, which is incremental as it extends Bayesian methods to handle uncertainty propagation in such contexts.
The paper tackles the problem of community detection in networks where edges are not directly observed, using a Bayesian hierarchical model that processes interdependent node signals to propagate uncertainties from raw data to community labels, achieving multiscale detection and optimal scale selection via model comparison, with applications to S&P100 stock returns and US climate data.
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.