16 citations · 38 across the 7 of their papers we have counts for
7 papers · 1 filter
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…
Approximate Latent Force Model Inference
Jacob D. Moss, Felix L. Opolka, Bianca Dumitrascu +1
Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems. They carry the structure of differential…
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…
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…
Spatio-Temporal Deep Graph Infomax
Felix L. Opolka, Aaron Solomon, Cătălina Cangea +3
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 ove…