Modeling Event Propagation via Graph Biased Temporal Point Process

Temporal point process is widely used for sequential data modeling. In this article, we focus on the problem of modeling sequential event propagation in graph, such as retweeting by social network users and news transmitting between websites. Given a collection of event propagation sequences, the conventional point process model considers 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. Moreover, the learned node embedding vector is also integrated into the embedded event history as side information. Experiments on a synthetic data set and two real-world data sets show the efficacy of our model compared with conventional methods and state-of-the-art ones.