Supervised Graph Inference
Abstract
We formulate the problem of graph inference where part of the graph is known as a supervised learning problem, and propose an algorithm to solve it. The method involves the learning of a mapping of the vertices to a Euclidean space where the graph is easy to infer, and can be formu- lated as an optimization problem in a reproducing kernel Hilbert space. We report encouraging results on the problem of metabolic network re- construction from genomic data.
Cite
Text
Vert and Yamanishi. "Supervised Graph Inference." Neural Information Processing Systems, 2004.Markdown
[Vert and Yamanishi. "Supervised Graph Inference." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/vert2004neurips-supervised/)BibTeX
@inproceedings{vert2004neurips-supervised,
title = {{Supervised Graph Inference}},
author = {Vert, Jean-philippe and Yamanishi, Yoshihiro},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1433-1440},
url = {https://mlanthology.org/neurips/2004/vert2004neurips-supervised/}
}