A Methodology for the Analysis of Collaboration Networks with Higher-Order Interactions

Abstract

Scientific research often involves collaboration among researchers, and coauthorship networks are a common means of exploring these collaborations. However, traditional coauthorship networks represent coauthorship relations using simple links, i.e., pairwise interactions, which fail to capture the strength of scientific collaborations in either small or large groups. In this study, we propose a novel methodology to address this issue, which involves using a multilayer network model that captures the strength of coauthorship relations and employs a convergence index to identify the collaboration order in which these properties converge. We apply this methodology to investigate the collaborative behavior of researchers in the context of the three main public universities in Mexico over the last decade, using Scopus data as the primary source of information. Our study reveals that community structure emerges in low-order collaborations, and higher-order collaborations lead to increased clustering and centrality measures. Our methodology provides a comprehensive and insightful way of analyzing scientific collaborations and sheds light on the dynamics of scientific collaboration, providing a valuable tool for future studies. Our proposed model and convergence index can be applied to other scientific domains to better capture the strength of collaborations among researchers.

Bibliometric and visualization analysis of the application of inorganic nanomaterials to autoimmune diseases

“…56 The most common bibliometric techniques mainly include co-authorship analysis, co-occurrence analysis, and co-citation analysis to determine the topics and research hot spots in the literature. 57 Co-authorship analysis reveals patterns of cooperation between authors, institutions and countries. Co-occurrence analysis uses the frequency of multiple words in the same essay to determine how close they are to each other, thus showing hot spots and trends in the discipline.…”

Research on User Behavior Based on Higher-Order Dependency Network

“…An HON is better able to mine historical information, and higher-order nodes are also able to show the sources of historical information [ 10 ]. An HON has higher accuracy improvement on sequence data with richer historical information and strong dependency [ 11 ], such as citation network data [ 12 , 13 ], trajectory data [ 14 ], etc. And there are also quite a few dependencies between user behaviors.…”