Web: http://arxiv.org/abs/2206.08252

June 17, 2022, 1:11 a.m. | Celia Hacker, Bastian Rieck

cs.LG updates on arXiv.org arxiv.org

Graph embedding techniques are a staple of modern graph learning research.
When using embeddings for downstream tasks such as classification, information
about their stability and robustness, i.e., their susceptibility to sources of
noise, stochastic effects, or specific parameter choices, becomes increasingly
important. As one of the most prominent graph embedding schemes, we focus on
node2vec and analyse its embedding quality from multiple perspectives. Our
findings indicate that embedding quality is unstable with respect to parameter
choices, and we propose strategies …

arxiv lg node2vec on

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