1 code implementation • 7 Nov 2020 • Karsten Borgwardt, Elisabetta Ghisu, Felipe Llinares-López, Leslie O'Bray, Bastian Rieck
Graph-structured data are an integral part of many application domains, including chemoinformatics, computational biology, neuroimaging, and social network analysis.
2 code implementations • ICLR 2022 • Leslie O'Bray, Max Horn, Bastian Rieck, Karsten Borgwardt
Graph generative models are a highly active branch of machine learning.
no code implementations • 27 Oct 2021 • Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data.
1 code implementation • 28 Oct 2021 • Michael F. Adamer, Edward De Brouwer, Leslie O'Bray, Bastian Rieck
Furthermore, we demonstrate practical use cases of magnitude for machine learning applications and propose a novel magnitude model that consists of a computationally efficient magnitude computation and a learnable metric.
3 code implementations • 7 Feb 2022 • Dexiong Chen, Leslie O'Bray, Karsten Borgwardt
Here, we show that the node representations generated by the Transformer with positional encoding do not necessarily capture structural similarity between them.
Ranked #4 on Graph Property Prediction on ogbg-code2
1 code implementation • 15 Jun 2022 • Renming Liu, Semih Cantürk, Frederik Wenkel, Sarah McGuire, Xinyi Wang, Anna Little, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry.