Graph Positional and Structural Encoder

1 code implementation • 14 Jul 2023 • Renming Liu, Semih Cantürk, Olivier Lapointe-Gagné, Vincent Létourneau, Guy Wolf, Dominique Beaini, Ladislav Rampášek

Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, as in general graphs lack a canonical node ordering.

Taxonomy of Benchmarks in Graph Representation Learning

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.

Benchmarking Graph Representation Learning

Towards a Taxonomy of Graph Learning Datasets

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.

Benchmarking Graph Learning

Machine-Learning Driven Drug Repurposing for COVID-19

no code implementations • 25 Jun 2020 • Semih Cantürk, Aman Singh, Patrick St-Amant, Jason Behrmann

Our in-silico approach to drug repurposing has promise in identifying new drug candidates and treatments for other viruses.

BIG-bench Machine Learning