论文标题
基于连续时间事件的时间网络的Hawkes边缘分区模型
The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks
论文作者
论文摘要
我们提出了一个新型的概率框架，以模拟连续时间交互事件数据。我们的目标是推断实体之间时间相互作用的基础的\ emph {隐式}社区结构，并利用社区结构如何影响这些节点之间的相互作用动态。为此，我们使用相互兴奋的霍克斯过程对个人之间的相互作用进行建模。每对个体的霍克斯过程的基本率是基于使用层次伽马过程边缘分区模型（HGAP-EPM）推断出的潜在表示。特别是，我们的模型允许每对个人之间的相互作用动态由各自的关联社区调节。此外，我们的模型可以灵活地合并辅助个人的属性，或与交互事件相关的协变量。有效的Gibbs采样和期望最大化算法是开发出通过Pólya-Gamma数据增强策略进行推论的。现实世界数据集的实验结果表明，与最先进的方法相比，我们的模型不仅可以实现时间链接预测的竞争性能，而且还发现了观察到的时间相互作用背后的可解释潜在结构。
We propose a novel probabilistic framework to model continuous-time interaction events data. Our goal is to infer the \emph{implicit} community structure underlying the temporal interactions among entities, and also to exploit how the community structure influences the interaction dynamics among these nodes. To this end, we model the reciprocating interactions between individuals using mutually-exciting Hawkes processes. The base rate of the Hawkes process for each pair of individuals is built upon the latent representations inferred using the hierarchical gamma process edge partition model (HGaP-EPM). In particular, our model allows the interaction dynamics between each pair of individuals to be modulated by their respective affiliated communities. Moreover, our model can flexibly incorporate the auxiliary individuals' attributes, or covariates associated with interaction events. Efficient Gibbs sampling and Expectation-Maximization algorithms are developed to perform inference via Pólya-Gamma data augmentation strategy. Experimental results on real-world datasets demonstrate that our model not only achieves competitive performance for temporal link prediction compared with state-of-the-art methods, but also discovers interpretable latent structure behind the observed temporal interactions.
