Graph classification aims to categorise graphs based on their structure and node attributes.
To address this, we present a novel, generalized kernel for graphs with node feature data for semi-supervised learning.
Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems.
We demonstrate that EGC outperforms existing approaches across 6 large and diverse benchmark datasets, and conclude by discussing questions that our work raise for the community going forward.
Ranked #11 on Graph Property Prediction on ogbg-code2
A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development projects is assessing the impact of planning and construction activities on mobility flows.
Social and Information Networks Physics and Society
Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes over time.