从交互数据中重建签署的关系
Georges Andres1, Giona Casiraghi1, Giacomo Vaccario1
1ETH Zürich, Chair of Systems Design, Weinbergstrasse 56/58, Zürich, Switzerland.
Scientific reports
|November 24, 2023
概括
研究人员现在可以使用交互数据来检测积极和消极的社会关系,而不仅仅是调查. 这种统计网络方法准确地重建了签署的关系,并揭示了社区的同源性和凝聚力.
科学领域:
- 社交网络分析 社交网络分析
- 计算社会科学 计算社会科学
- 社会学 社会学 社会学
背景情况:
- 签名社会关系 (积极/消极) 对人类行为和社区结构至关重要.
- 关于签约关系的数据很少,通常依赖于自我报告的调查.
- 丰富的交互数据 (例如近距离,沟通) 没有被用来推断签名关系.
研究的目的:
- 证明从交互数据中提取潜在的签名社会关系.
- 为了验证推断的签名关系与自我报告数据的准确性.
- 通过重建的签名网络来调查同志性和群体凝聚力.
主要方法:
- 应用统计网络方法来构建签名关系网络.
- 从交互数据 (例如,近距离,通信) 中推断出签名关系.
- 分析了基于性别,宗教信仰和财务背景的同性恋.
- 在重建的网络中使用三元统计数据评估了群体凝聚力.
主要成果:
- 在五个不同的社区成功重建了签名关系网络.
- 推断的关系与个人自我报告的关系有很强的对应性.
- 确定了同类关系模式,并分析了社区内的群体凝聚力.
结论:
- 交互数据可以有效地用于推断和分析签名的社会关系.
- 统计网络方法为绘制社区社会结构提供了一个强大的方法.
- 这种方法学为研究社会动态,同类性和凝聚力开辟了新的途径.
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