A kernel method for unsupervised structured network inference

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Item Type:	Article
Title:	A kernel method for unsupervised structured network inference
Creators Name:	Lippert, C., Stegle, O., Ghahramani, Z. and Borgwardt, K.M.
Abstract:	Network inference is the problem of inferring edges between a set of real-world objects, for instance, interactions between pairs of proteins in bioinformatics. Current kernelbased approaches to this problem share a set of common features: (i) they are supervised and hence require labeled training data; (ii) edges in the network are treated as mutually independent and hence topological properties are largely ignored; (iii) they lack a statistical interpretation. We argue that these common assumptions are often undesirable for network inference, and propose (i) an unsupervised kernel method (ii) that takes the global structure of the network into account and (iii) is statistically motivated. We show that our approach can explain commonly used heuristics in statistical terms. In experiments on social networks, different variants of our method demonstrate appealing predictive performance.
Source:	Journal of Machine Learning Research
ISSN:	1532-4435
Volume:	5
Page Range:	368-375
Date:	2009
Official Publication:	http://proceedings.mlr.press/v5/lippert09a.html

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