Year: 2026
Type: article
Abstract: Probabilistic Inference on Networks (ProbINet) is a Python package that provides a unified framework to perform probabilistic inference on networks, enabling researchers and practitioners to analyze and model complex network data.The package integrates code implementations from several scientific publications, supporting tasks such as community detection, anomaly detection, and synthetic data generation using latent variable models.It is designed to simplify the use of cutting-edge techniques in network analysis by providing a cohesive and user-friendly interface. Statement of needNetwork analysis is central to disciplines such as social sciences, biology, and fraud detection, where understanding relationships is essential.Probabilistic generative models (Contisciani et al., 2020(Contisciani et al., , 2022;;Safdari et al., 2021 Safdari et al., , 2022;; Safdari & De Bacco, 2022) reveal hidden patterns, detect communities, identify anomalies, and generate synthetic data.Their broader use is limited by fragmented implementations that hinder comparisons and reproducibility.ProbINet addresses this gap by unifying recent approaches in a single framework, improving accessibility and usability across disciplines.
DOI: 10.21105/joss.08638
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7203699501