  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.
