This study investigates in-depth information about the factors influencing the digital
transformation of an educational establishment to becoming a high-performance education …

Graph learning is the fundamental task of estimating unknown graph connectivity from
available data. Typical approaches assume that not only is all information available …

This paper introduces a probabilistic approach for tracking the dynamics of unweighted and
directed graphs using state-space models (SSMs). Unlike conventional topology inference …

This paper addresses the issue of limited data in financial time series forecasting, a
challenge regularly faced in the context of newly listed companies. While deep learning …

Inference and data analysis over networks have become significant areas of research due to
the increasing prevalence of interconnected systems and the growing volume of data they …