Dynamic network data have become ubiquitous in social network analysis, with new information becoming available that captures when friendships form, when corporate transactions happen and when countries interact with each other. Flexible and interpretable models are needed in order to properly capture the behavior of individuals in such networks. In this paper, we focus on study the underlying latent space that describes the social properties of a dynamic and directed international relations network of countries. We extend the directed additive and multiplicative effects network model to the continuous time setting by treating the time-evolution of model parameters using Gaussian processes. Importantly we incorporate both time-varying covariates and node-level additive random effects that aid in increasing model realism. We demonstrate the usefulness and flexibility of this model on a longitudinal dataset of formal state visits between the world's 18 largest economies. Not only does the model offer high quality predictive accuracy, but the latent parameters naturally map onto world events that are not directly measured in the data.

翻译：动态网络数据在社交网络分析中已变得无处不在，这些新数据捕捉了友谊形成、企业交易发生以及国家间互动的时刻。为了准确描述此类网络中个体的行为，需要灵活且可解释的模型。本文重点研究描述动态、有向的国际关系国家网络社会属性的潜空间。我们通过使用高斯过程处理模型参数的时间演化，将定向加性乘性效应网络模型扩展到连续时间设置中。重要的是，我们引入了时变协变量和节点级加性随机效应，这有助于提高模型的现实性。我们在全球18个最大经济体之间的正式国事访问纵向数据集上证明了该模型的有效性和灵活性。该模型不仅提供了高质量的预测精度，而且潜参数自然地映射到数据中未直接测量的世界事件上。