Web: http://arxiv.org/abs/2206.07729

June 17, 2022, 1:10 a.m. | Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O'Bray, Michael Perlmutter,

cs.LG updates on arXiv.org arxiv.org

Graph Neural Networks (GNNs) extend the success of neural networks to
graph-structured data by accounting for their intrinsic geometry. While
extensive research has been done on developing GNN models with superior
performance according to a collection of graph representation learning
benchmarks, it is currently not well understood what aspects of a given model
are probed by them. For example, to what extent do they test the ability of a
model to leverage graph structure vs. node features? Here, we develop …

arxiv benchmarks graph learning lg representation representation learning taxonomy

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY