带有个体特异效应的边际模型用于对纵向二分网的分析
Francesco Bartolucci1, Antonietta Mira2,3, Stefano Peluso2,4
1Department of Economics, University of Perugia, Via A. Pascoli, 20, 06123 Perugia, Italy.
概括
本研究引入了基于事件数据分析双边社交网络的新模型. 它有助于理解演员的合作和参与趋势随着时间的推移.
科学领域:
- 社交网络分析 社交网络分析
- 统计建模 统计建模
- 网络科学 网络科学
背景情况:
- 双边社交网络是复杂的系统.
- 随着时间的推移,分析关系事件带来了挑战.
- 现有的模型可能无法完全捕捉演员行为动态.
研究的目的:
- 为双边社会网络提出一个新的建模框架.
- 分析部分时间顺序的关系事件的序列.
- 解释参与者参与和合作趋势.
主要方法:
- 直接建模事件中的参与者参与的联合分布.
- 使用基于一级和二级效应的参数化.
- 采用复合概率推断和分类来进行行为者聚类.
主要成果:
- 该模型有效地代表了潜在的个人行为轨迹.
- 第二阶效应揭示了合作倾向.
- 一级效应表明个人参与倾向.
- 该方法在模拟和真实世界的数据上得到验证.
结论:
- 拟议的框架提供了一种强大的方法来分析动态的双边社交网络.
- 它为演员行为和网络结构提供了可解释的见解.
- 适用于各种领域,包括科学合作分析.
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