Hybrid Bayesian Models for Community Detection with Application to a Colombian Conflict Network

TL;DR

This paper presents a Bayesian clustering framework for undirected binary networks, introducing two hybrid models that capture complex relational patterns, and applies them to analyze Colombian conflict networks.

Contribution

It develops two novel hybrid Bayesian models extending the stochastic block model to better capture complex network structures, with applications to real-world conflict data.

Findings

Models successfully identify clusters aligned with territorial control and trafficking routes.

Bayesian inference via MCMC effectively recovers underlying community structures.

Framework performs well on synthetic and benchmark datasets.

Abstract

We introduce a flexible Bayesian framework for clustering nodes in undirected binary networks, motivated by the need to uncover structural patterns in complex environments. Building on the stochastic block model, we develop two hybrid extensions: the Class-Distance Model, which governs interaction probabilities through Euclidean distances between cluster-level latent positions, and the Class-Bilinear Model, which captures more complex relational patterns via bilinear interactions. We apply this framework to a novel network derived from the Colombian armed conflict, where municipalities are connected through the co-presence of armed actors, violence, and illicit economies. The resulting clusters align with empirical patterns of territorial control and trafficking corridors, highlighting the models' capacity to recover and explain complex dynamics. Full Bayesian inference is carried out via MCMC under both finite and nonparametric clustering priors. While the main application centers on the Colombian conflict, we also assess model performance using synthetic data as well as other two benchmark datasets.