Web: http://arxiv.org/abs/2205.02998

May 9, 2022, 1:11 a.m. | Beidi Zhao, Boxin Du, Zhe Xu, Liangyue Li, Hanghang Tong

cs.LG updates on arXiv.org arxiv.org

Graph Neural Networks (GNNs) have achieved tremendous success in a variety of
real-world applications by relying on the fixed graph data as input. However,
the initial input graph might not be optimal in terms of specific downstream
tasks, because of information scarcity, noise, adversarial attacks, or
discrepancies between the distribution in graph topology, features, and
groundtruth labels. In this paper, we propose a bi-level optimization-based
approach for learning the optimal graph structure via directly learning the
Personalized PageRank propagation matrix …

arxiv graph graph neural networks learning networks neural neural networks

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

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote

Director of AI/ML Engineering

@ Armis Industries | Remote (US only), St. Louis, California

Digital Analytics Manager

@ Patagonia | Ventura, California