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Empirical Inference Conference Paper 2006

Information Marginalization on Subgraphs

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Empirical Inference
Jiayuan Huang
  • Doctoral Researcher
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Empirical Inference
Dengyong Zhou

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.

Author(s): Huang, J. and Zhu, T. and Rereiner, R. and Zhou, D. and Schuurmans, D.
Book Title: ECML/PKDD 2006
Journal: Knowledge Discovery in Databases: PKDD 2006
Pages: 199-210
Year: 2006
Month: September
Day: 0
Editors: F{\"u}rnkranz, J. , T. Scheffer, M. Spiliopoulou
Publisher: Springer
Bibtex Type: Conference Paper (inproceedings)
Address: Berlin, Germany
DOI: 10.1007/11871637_22
Event Name: 10th European Conference on Principles and Practice of Knowledge Discovery in Databases
Event Place: Berlin, Germany
Digital: 0
Electronic Archiving: grant_archive
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex 

@inproceedings{4163,
  title = {Information Marginalization on Subgraphs},
  journal = {Knowledge Discovery in Databases: PKDD 2006},
  booktitle = {ECML/PKDD 2006},
  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.},
  pages = {199-210},
  editors = {F{\"u}rnkranz, J. , T. Scheffer, M. Spiliopoulou},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2006},
  slug = {4163},
  author = {Huang, J. and Zhu, T. and Rereiner, R. and Zhou, D. and Schuurmans, D.},
  month_numeric = {9}
}