扩展定向模糊社交网络分析:在中国职业教育课程网络的框架和应用
Borui Zuo1, Keqi Shang2, Jie Zhang1
1School of Rail Transit, Yunnan Vocational College of Transportation, Kunming, China.
PloS one
|October 27, 2025
概括
本研究介绍了一种扩展定向模糊社交网络分析框架 (EFDSNAF),用于处理复杂,模糊的网络. 该框架有效地确定了职业教育课程中的核心课程和学科差异.
科学领域:
- 社交网络分析 社交网络分析
- 模糊的数学 模糊的数学
- 教育数据挖掘教育数据挖掘
背景情况:
- 传统的模糊社交网络分析 (FSNA) 与网络异质性和关系模糊性作斗争.
- 现有的模糊中心性措施缺乏适应多样化的网络特征和关系密切性的适应性.
研究的目的:
- 提出一个扩展的定向模糊社交网络分析框架 (EFDSNAF),解决网络异质性和模糊性问题.
- 通过引入路径的总模糊强度 (TFIP) 和优化计算方法来增强模糊中心性指标.
主要方法:
- 扩展定向模糊社交网络分析框架 (EFDSNAF) 的开发,包括典型连接.
- 路径的总模糊强度 (TFIP) 的介绍和定义,用于增强模糊中心性计算.
- 模糊之间的优化和接近的中心性计算,用示例验证.
主要成果:
- EFDSNAF成功评估了模糊网络,并确定了中国职业教育课程网络中的核心课程.
- 城市铁路运输运营和管理专业 (URTOMM) 和城市铁路运输通信和信号技术专业 (URTCSTM) 的分析揭示了明显的学科差异.
- 优化的模糊中心性措施有效地捕获了网络结构和关系.
结论:
- EFDSNAF为复杂的模糊社交网络提供了强大的分析框架,特别是在教育环境中.
- 拟议的TFIP和优化的中心性措施提高了网络分析的精度,揭示了关键的结构洞察力.
- 该框架在确定核心学科和理解学科间关系方面表现出显著的有效性.
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