论文标题
动态网络的潜在空间模型
Latent Space Models for Dynamic Networks
论文作者
论文摘要
动态网络用于各种领域，以表示实体之间关系的结构和演变。我们提出了一个模型，该模型将纵向网络数据嵌入潜在的欧几里得空间中。提出了马尔可夫链蒙特卡洛算法来估计参与者在网络中的模型参数和潜在位置。该模型产生了动态网络的有意义的可视化，使研究人员洞悉了网络的局部和全局结构。该模型处理定向或无向的边缘，很容易处理缺失的边缘，并且可以很好地预测未来的边缘。此外，还提供了一种新颖的方法来检测和可视化参与者之间仅使用边缘信息之间的吸引人影响。我们使用案例对照可能性近似来加快估计算法，从而稍微修改以说明丢失的数据。我们将潜在空间模型应用于从荷兰教室收集的数据，以及对美国众议院成员收集的共同赞助网络，通过对网络的了解来说明该模型的有用性。
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.
