论文标题
具有加权边缘的动态网络的潜在空间模型
Latent Space Models for Dynamic Networks with Weighted Edges
论文作者
论文摘要
通过为动态网络实现潜在空间模型，可以更好地理解纵向二进制关系数据。通过使用链接函数对二元组的平均值进行建模或通过数据扩展采用类似的策略，可以将这种方法广泛扩展到许多类型的加权边缘。为了证明这一点，我们提出了计数二元组和非阴性真实二元组的模型，分析模拟数据以及手机数据和世界出口/进口数据。由马尔可夫链蒙特卡洛算法估算的模型参数和潜在参与者的轨迹可洞悉网络动力学。
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.
