Correlations between anomalous activity patterns can yield pertinent information about complex social processes: a significant deviation from normal behavior, exhibited simultaneously by multiple pairs of actors, provides evidence for some underlying relationship involving those pairs---i.e., a multilateral relation. We introduce a new nonparametric Bayesian latent variable model that explicitly captures correlations between anomalous interaction counts and uses these shared deviations from normal activity patterns to identify and characterize multilateral relations. We showcase our model's capabilities using the newly curated Global Database of Events, Location, and Tone, a dataset that has seen considerable interest in the social sciences and the popular press, but which has is largely unexplored by the machine learning community. We provide a detailed analysis of the latent structure inferred by our model and show that the multilateral relations correspond to major international events and long-term international relationships. These findings lead us to recommend our model for any data-driven analysis of interaction networks where dynamic interactions over the edges provide evidence for latent social structure.

介绍了一种新的非参数贝叶斯潜变量模型，该模型明确捕捉了异常交互计数之间的相关性，并利用这些共享的偏差从正常活动模式中识别和表征多边关系。通过使用新近策划的全球事件位置与语气数据库，显示了该模型的能力，并在研究结果中指出多边关系对应于重大国际事件和长期国际关系，因此建议在数据驱动的交互网络分析中应用该模型。

从动态配对互动推断多边关系