Supervised Graph Inference

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 formulated as an optimization problem in a reproducing kernel Hilbert space. We report encouraging results on the problem of metabolic network reconstruction from genomic data.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Jean-Philippe Vert, Yoshihiro Yamanishi
• Keywords: Graph Inference, NIPS 2004, NIPS 2007, Reproducing Kernel Hilbert Space, Supervised Learning Problem