Extended Directed Fuzzy Social Network Analysis: A framework and application to curriculum networks in Chinese vocational education

Abstract

Due to the differences in node types and the diversity of network relationships, Fuzzy Social Network Analysis (FSNA) needs to specifically address the issues of network heterogeneity and relationship ambiguity. To address this challenge, we propose a new analytical framework called Extended Directed Fuzzy Social Network Analysis Framework (EFDSNAF), which establishes the Typical Connections to assist in evaluating the fuzzy network. Meanwhile, in the area of fuzzy centrality measures, we enhance the variability of the Fuzzy Intensity of Path and propose the term “Total Fuzzy Intensity of Path” (TFIP), considering the distinct characteristics of different networks may lead to variations in path intensity expressions and differences in closeness relationships. Based on this, we optimize the computational methods for fuzzy betweenness centrality and fuzzy closeness centrality, with the efficacy of the method being demonstrated through two examples. Then we applied EDFSNAF to analyze Chinese vocational education curriculum network, with empirical investigation on the Urban Rail Transit Operation and Management Major (URTOMM) and Urban Rail Transit Communication and Signaling Technology Major (URTCSTM). Through EDFSNAF, core courses were identified, and network metrics for different majors effectively captured essential disciplinary differences between the two fields, clearly demonstrating the effectiveness of EDFSNAF.