基于图偏置时间点过程的事件传播模型
Modeling Event Propagation via Graph Biased Temporal Point Process
摘要：
时态点过程是序列数据建模的常用方法。本文重点研究了图中事件传播的建模问题，如社交网络用户的转发、网站间的新闻传播等。给定一组事件传播序列，传统的点过程模型只考虑事件历史，即将事件历史嵌入到一个向量中，而不考虑潜在图结构。提出了一种基于图表示学习的结构化时间点过程（GBTPP），分别对节点间的直接影响和事件历史的间接影响进行建模。此外，学习的节点嵌入向量也被集成到嵌入的事件历史作为边信息。在一个合成数据集和两个真实数据集上的实验表明，与传统方法和最先进的方法相比，我们的模型是有效的。
英文摘要：
Temporal point process is widely used for sequential data modeling. In this paper, we focus on the problem of modeling sequential event propagation in graph, such as retweeting by social network users, news transmitting between websites, etc. Given a collection of event propagation sequences, conventional point process model consider only the event history, i.e. embed event history into a vector, not the latent graph structure. We propose a Graph Biased Temporal Point Process (GBTPP) leveraging the structural information from graph representation learning, where the direct influence between nodes and indirect influence from event history is modeled respectively. Moreover, the learned node embedding vector is also integrated into the embedded event history as side information. Experiments on a synthetic dataset and two real-world datasets show the efficacy of our model compared to conventional methods and state-of-the-art.
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