10 citations · 23 across the 8 of their papers we have counts for
8 papers
Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations
Richard Bergna, Sergio Calvo-Ordoñez, Felix L. Opolka +2
We propose a novel Stochastic Differential Equation (SDE) framework to address the problem of learning uncertainty-aware representations for graph-structured data. While Graph Neur…
Graph Neural Stochastic Differential Equations
Richard Bergna, Felix Opolka, Pietro Liò +1
We present a novel model Graph Neural Stochastic Differential Equations (Graph Neural SDEs). This technique enhances the Graph Neural Ordinary Differential Equations (Graph Neural…
Graph Classification Gaussian Processes via Spectral Features
Felix L. Opolka, Yin-Cong Zhi, Pietro Liò +1
Graph classification aims to categorise graphs based on their structure and node attributes. In this work, we propose to tackle this task using tools from graph signal processing b…
Transductive Kernels for Gaussian Processes on Graphs
Yin-Cong Zhi, Felix L. Opolka, Yin Cheng Ng +2
Kernels on graphs have had limited options for node-level problems. To address this, we present a novel, generalized kernel for graphs with node feature data for semi-supervised le…
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets
Felix L. Opolka, Yin-Cong Zhi, Pietro Liò +1
Graph-based models require aggregating information in the graph from neighbourhoods of different sizes. In particular, when the data exhibit varying levels of smoothness on the gra…
Do We Need Anisotropic Graph Neural Networks?
Shyam A. Tailor, Felix L. Opolka, Pietro Liò +1
Common wisdom in the graph neural network (GNN) community dictates that anisotropic models -- in which messages sent between nodes are a function of both the source and target node…
Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks
Gevorg Yeghikyan, Felix L. Opolka, Mirco Nanni +2
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 constructi…
Graph Convolutional Gaussian Processes For Link Prediction
Felix L. Opolka, Pietro Liò
Link prediction aims to reveal missing edges in a graph. We address this task with a Gaussian process that is transformed using simplified graph convolutions to better leverage the…