Modeling Event Propagation via Graph Biased Temporal Point Process
This work addresses event propagation modeling in social networks and similar domains, representing an incremental improvement by integrating graph structure into existing temporal point process frameworks.
The paper tackles the problem of modeling sequential event propagation in graphs by proposing a Graph Biased Temporal Point Process (GBTPP) that incorporates latent graph structure and node embeddings, achieving improved performance over conventional and state-of-the-art methods on synthetic and real-world datasets.
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.