Marked Neural Spatio-Temporal Point Process Involving a Dynamic Graph Neural Network

Published: 20 Oct 2023, Last Modified: 08 Nov 2023TGL Workshop 2023 ShortPaperEveryoneRevisionsBibTeX
Keywords: Neural Temporal Point Process, Dynamic Graph Neural Network, Graph Stream, Spatio-Temporal Point Process, Marked Temporal Point Process
Abstract: Spatio-Temporal Point Processes (STPPs) have recently become increasingly interesting for learning dynamic graph data since many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are naturally related and dynamic. While training Recurrent Neural Networks and solving PDEs for representing temporal data is expensive, TPPs were a good alternative. The drawback is that constructing an appropriate TPP for modeling temporal data requires the assumption of a particular temporal behavior of the data. To overcome this problem, Neural TPPs have been developed that enable learning of the parameters of the TPP. However, the research is relatively young for modeling dynamic graphs, and only a few TPPs have been proposed to handle edge-dynamic graphs. To allow for learning on a fully dynamic graph, we propose the first Marked Neural Spatio-Temporal Point Process (MNSTPP) that leverages a Dynamic Graph Neural Network to learn Spatio-TPPs to model and predict any event in a graph stream. In addition, our model can be updated efficiently by considering single events for local retraining.
Format: Long paper, up to 8 pages. If the reviewers recommend it to be changed to a short paper, I would be willing to revise my paper to fit within 4 pages.
Submission Number: 29