ArticlePloS one2025
Extended Directed Fuzzy Social Network Analysis: A framework and application to curriculum networks in Chinese vocational education.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.