A Regularization Framework for Learning from Graph Data

Zhou, D; Schölkopf, B

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A Regularization Framework for Learning from Graph Data

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Zhou, D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zhou-Schoelkopf-2004.pdf
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Citation

Zhou, D., & Schölkopf, B. (2004). A Regularization Framework for Learning from Graph Data. In ICML 2004 Workshop on Statistical Relational Learning and Its Connections to Other Fields (SRL 2004) (pp. 132-137).

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-F3AB-E

Abstract

The data in many real-world problems can be thought of as a graph, such as the web, co-author networks, and biological networks. We propose a general regularization framework on graphs, which is applicable to the classification, ranking, and link prediction
problems. We also show that the method can be explained as lazy
random walks. We evaluate the method on a number of experiments.