Dynamic networks are used in a variety of fields to represent the structure
and evolution of the relationships between entities. We present a model which
embeds longitudinal network data as trajectories in a latent Euclidean space. A
Markov chain Monte Carlo algorithm is proposed to estimate the model parameters
and latent positions of the actors in the network. The model yields meaningful
visualization of dynamic networks, giving the researcher insight into the
evolution and the structure, both local and global, of the network. The model
handles directed or undirected edges, easily handles missing edges, and lends
itself well to predicting future edges. Further, a novel approach is given to
detect and visualize an attracting influence between actors using only the edge
information. We use the case-control likelihood approximation to speed up the
estimation algorithm, modifying it slightly to account for missing data. We
apply the latent space model to data collected from a Dutch classroom, and a
cosponsorship network collected on members of the U.S. House of
Representatives, illustrating the usefulness of the model by making insights
into the networks.

本研究利用潜在欧氏空间嵌入纵向网络数据轨迹，提出了一种动态网络模型，采用 MCMC 算法估计模型参数和演员的潜在位置。该模型具有有意义的可视化效果，易于处理方向或无向边，缺失边且很好地用于预测未来边。此外，我们给出了一种新颖的方法来仅使用边信息来检测和可视化演员之间的吸引力。我们使用该模型研究了从荷兰教室和美国众议院成员收集的协同赞助网络中收集的数据，以展示该模型的实用性并使研究人员深入了解网络的演化和结构。