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  A Regularization Framework for Learning from Graph Data

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

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-F3AB-E Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-F3AC-C
Genre: Conference Paper

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 Creators:
Zhou, D1, Author           
Schölkopf, B1, Author           
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 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.

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 Dates: 2004
 Publication Status: Issued
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 Identifiers: BibTex Citekey: 2688
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Title: ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields
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Title: ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 132 - 137 Identifier: -