Unveiling collaboration persistence and interactions among Italian academic statisticians through relational hyperevent models

In the last years, there has been a growing interest in the significance of scientific collaboration. Several studies have analyzed co-authorship networks as a convenient tool to investigate collaboration among scientists across different disciplines and over time. Co-authorship networks among scientists can be represented by hypergraphs, where each scientific paper is the corresponding hyperevent. To account for this scenario and evaluate the evolution of the co-authorship network over time, Relational Hyperevent Models (RHEM) have been recently proposed. Such models can handle time-varying data and events that occur in hyperedges (a set of subsets of vertices in a hypergraph that can connect more than two variables), allowing modeling events composed of any authors, analyze polyadic settings, and consider subgroup persistence over time. This paper aims to apply such models to analyze the evolution of collaboration among the Italian academic statisticians, affiliated to five scientific sub-sectors, observed from 2012 to 2022, including their external co-authors. Bibliographic information has been retrieved from the Scopus database. Different model specifications are proposed to evaluate the tendency to maintain collaboration between authors over time (familiarity effects) and/or the tendency to create new collaborations with authors who co-authored with common groups of authors (closure effects).