# Community detection in networks without observing edges

arXiv: https://arxiv.org/abs/1808.06079 · HTML: https://mlsanity.com/paper/1808.06079/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Unifying Pairwise Interactions in Complex Dynamics](https://mlsanity.com/paper/2201.11941/) (2022)
- [Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths](https://mlsanity.com/paper/2112.00183/) (2021)
- [Geodesic Length Distribution in Sparse Network Ensembles](https://mlsanity.com/paper/2111.02330/) (2021)
- [A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty](https://mlsanity.com/paper/2304.04497/) (2023)

## Abstract (arXiv)

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.