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Found 8 papers, 3 papers with code
Graph Classification Gaussian Processes via Spectral Features
Graph classification aims to categorise graphs based on their structure and node attributes.
Transductive Kernels for Gaussian Processes on Graphs
To address this, we present a novel, generalized kernel for graphs with node feature data for semi-supervised learning.
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets
Graph-based models require aggregating information in the graph from neighbourhoods of different sizes.
Approximate Latent Force Model Inference
Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems.
Do We Need Anisotropic Graph Neural Networks?
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
Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks
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
Graph Convolutional Gaussian Processes For Link Prediction
Link prediction aims to reveal missing edges in a graph.
Spatio-Temporal Deep Graph Infomax
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.
