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Conference Paper

Information Marginalization on Subgraphs

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83983

Huang,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84330

Zhu T, Rereiner R, Zhou,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Huang, J., Zhu T, Rereiner R, Zhou, D., & Schuurmans, D. (2006). Information Marginalization on Subgraphs. Knowledge Discovery in Databases: PKDD 2006, 199-210.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D04B-2
Abstract
Real-world data often involves objects that exhibit multiple relationships; for example, ‘papers’ and ‘authors’ exhibit both paper-author interactions and paper-paper citation relationships. A typical learning problem requires one to make inferences about a subclass of objects (e.g. ‘papers’), while using the remaining objects and relations to provide relevant information. We present a simple, unified mechanism for incorporating information from multiple object types and relations when learning on a targeted subset. In this scheme, all sources of relevant information are marginalized onto the target subclass via random walks. We show that marginalized random walks can be used as a general technique for combining multiple sources of information in relational data. With this approach, we formulate new algorithms for transduction and ranking in relational data, and quantify the performance of new schemes on real world data—achieving good results in many problems.