Supervised Graph Inference

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Jean-philippe Vert, Yoshihiro Yamanishi


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.