A Comparative Study of a Co-authorship System of MURJ and JLT Using Network Approach

Abstract

A variety of complex real-world systems are analyzed as networks to better understand their heterogeneous and dynamic behaviors. This research implements a network science approach to model the co-authorship datasets of MURJ and JLT. Utilizing metrics such as closeness, betweenness, and clustering coefficients, the study determines that these collaboration networks are highly clustered with minimal path lengths between researchers. Unlike random networks, these journals possess an inhomogeneous distribution where a small number of central authors maintain a dominant influence across multiple issues. These structural characteristics highlight the non-random, organized nature of academic partnerships within these journals.