Community detection in networks without observing edges

2018/08/18 by Till Hoffmann, Leto Peel, Renaud Lambiotte +1 · 1 voice · 2 citations
Computer Science · Physics and Astronomy · #cs.LG #cs.SI #physics.soc-ph

paper · pdf · doi:10.1126/sciadv.aav1478

published as Science Advances 6(4) eaav1478, 2020 · 16 pages, 7 figures

arxiv published 2018/08/18 · arxiv created 2020/02/11 · arxiv updated 2020/02/12

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.

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• Machine Learning Informed by Micro and Mesoscopic Statistical Physics Methods for Community Detection

Discussions

• Many thanks to @PiratePeel for virtually joining @svscarpino and @NUnetsi students to discuss his paper "Community detection in networks with unobserved edges." Take away: We don't need edges if our [bsky, 0 points, 0 comments]

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