Inferring signed social networks from contact patterns
For researchers studying social networks from indirect observations, this method provides a way to infer signed ties (positive/negative) from contact patterns, addressing a key limitation of existing approaches.
The authors developed a Bayesian framework with MCMC inference to infer signed social networks from contact patterns, distinguishing between absent relationships and negative ties. Their method outperforms baselines in detecting negative edges on synthetic data and reveals structure consistent with friendship surveys in French high school contact data.
Social networks are typically inferred from indirect observations, such as proximity data; yet, most methods cannot distinguish between absent relationships and actual negative ties, as both can result in few or no interactions. We address the challenge of inferring signed networks from contact patterns while accounting for whether lack of interactions reflect a lack of opportunity as opposed to active avoidance. We develop a Bayesian framework with MCMC inference that models interaction groups to separate chance from choice when no interactions are observed. Validation on synthetic data demonstrates superior performance compared to natural baselines, particularly in detecting negative edges. We apply our method to French high school contact data to reveal a structure consistent with friendship surveys and demonstrate the model's adequacy through posterior predictive checks.