带权边动态网络的潜空间模型
Latent Space Models for Dynamic Networks with Weighted Edges
摘要：
通过实现动态网络的潜在空间模型，可以更好地理解纵向二元关系数据。这种方法可以广泛地扩展到许多类型的加权边缘，通过使用链接函数来模拟二元的平均值，或通过采用类似的策略，通过数据增强。为了证明这一点，我们提出了计数二元和非负实数二元模型，分析模拟数据，也包括手机数据和世界出口/进口数据。通过马尔可夫链蒙特卡罗算法估计的模型参数和潜在参与者的轨迹，提供了对网络动态的洞察。
英文摘要：
Longitudinal binary relational data can be better understood by implementing a latent space model for dynamic networks. This approach can be broadly extended to many types of weighted edges by using a link function to model the mean of the dyads, or by employing a similar strategy via data augmentation. To demonstrate this, we propose models for count dyads and for non-negative real dyads, analyzing simulated data and also both mobile phone data and world export/import data. The model parameters and latent actors' trajectories, estimated by Markov chain Monte Carlo algorithms, provide insight into the network dynamics.
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