no code implementations • 28 Aug 2024 • Richard Bergna, Sergio Calvo-Ordoñez, Felix L. Opolka, Pietro Liò, Jose Miguel Hernandez-Lobato
We address the problem of learning uncertainty-aware representations for graph-structured data.
no code implementations • 6 Jun 2023 • Felix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong
Graph classification aims to categorise graphs based on their structure and node attributes.
no code implementations • 28 Nov 2022 • Yin-Cong Zhi, Felix L. Opolka, Yin Cheng Ng, Pietro Liò, Xiaowen Dong
To address this, we present a novel, generalized kernel for graphs with node feature data for semi-supervised learning.
no code implementations • 25 Oct 2021 • Felix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong
Graph-based models require aggregating information in the graph from neighbourhoods of different sizes.
1 code implementation • 24 Sep 2021 • Jacob D. Moss, Felix L. Opolka, Bianca Dumitrascu, Pietro Lió
Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems.
2 code implementations • 3 Apr 2021 • Shyam A. Tailor, Felix L. Opolka, Pietro Liò, Nicholas D. Lane
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
1 code implementation • 24 Apr 2020 • Gevorg Yeghikyan, Felix L. Opolka, Mirco Nanni, Bruno Lepri, Pietro Lio'
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
no code implementations • 11 Feb 2020 • Felix L. Opolka, Pietro Liò
Link prediction aims to reveal missing edges in a graph.
no code implementations • 12 Apr 2019 • Felix L. Opolka, Aaron Solomon, Cătălina Cangea, Petar Veličković, Pietro Liò, R. Devon Hjelm
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.