A Latent Variable Model for Relational Events with Multiple Receivers
具有多个接收者的关系事件潜变量模型AI 解读 ↓
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具有多个接收者的关系事件潜变量模型
- 研究问题
- 如何同时分析一对一和一对多的有向关系事件，利用接收者集合的信息揭示网络成员角色，并描述发送与接收行为的未观测异质性？
- 方法与思路
- 作者以乘法潜在因子刻画发送者、接收者和消息异质性，通过适合度阈值生成接收者集合，用 Gibbs 抽样拟合并开展后验预测检查。
- 材料陈述的贡献
- 摘要称，二维潜变量模型能较好捕捉安然邮件中常见消息的接收人数分布及集合构成，并显示成员作为发送者和接收者的角色相近但不完全相同。
- 阅读关注点
- 应核对接收阈值与潜变量的识别、消息依赖如何处理、抽样诊断和后验预测标准，以及对常见消息的拟合能否覆盖较少见的互动。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实。摘要未说明完整识别条件、时间依赖处理或外部验证，拟合结果不能直接解释为互动因果机制。
原文摘要
Directional relational event data, such as email data, often contain unicast messages (i.e., messages of one sender towards one receiver) and multicast messages (i.e., messages of one sender towards multiple receivers). The Enron email data that is the focus in this paper consists of 31% multicast messages. Multicast messages contain important information about the roles of actors in the network, which is needed for better understanding social interaction dynamics. In this paper a multiplicative latent factor model is proposed to analyze such relational data. For a given message, all potential receiver actors are placed on a suitability scale, and the actors are included in the receiver set whose suitability score exceeds a threshold value. Unobserved heterogeneity in the social interaction behavior is captured using a multiplicative latent factor structure with latent variables for actors (which differ for actors as senders and receivers) and latent variables for individual messages. A Bayesian computational algorithm, which relies on Gibbs sampling, is proposed for model fitting. Model assessment is done using posterior predictive checks. Based on our analyses of the Enron email data, a mc-amen model with a 2 dimensional latent variable can accurately capture the empirical distribution of the cardinality of the receiver set and the composition of the receiver sets for commonly observed messages. Moreover the results show that actors have a comparable (but not identical) role as a sender and as a receiver in the network.