ProbINet: Bridging Usability Gaps in Probabilistic Network Analysis

Our goal is to provide a unified resource that brings together different advances scattered across many code repositories. By doing so, we aim not only to enhance the usability of existing models but also to facilitate the comparison of different approaches.

Moreover, through a range of tutorials, we aim at simplifying the use of these methods to perform inferential tasks, including the prediction of missing network edges, node clustering (community detection), anomaly identification, and the generation of synthetic data from latent variables.