Modeling Event Propagation via Graph Biased Temporal Point Process

TL;DR

This paper introduces a Graph Biased Temporal Point Process (GBTPP) that incorporates graph structure into modeling event propagation sequences, improving prediction accuracy over traditional methods.

Contribution

The paper presents a novel model that integrates graph structure learning with temporal point processes to better capture influence dynamics in event propagation.

Findings

GBTPP outperforms conventional models on synthetic and real datasets.

Incorporating graph structure improves event influence modeling.

Node embeddings enhance the representation of event history.

Abstract

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.