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Information Marginalization on Subgraphs


Conference Paper


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

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/11871637_22
Event Name: 10th European Conference on Principles and Practice of Knowledge Discovery in Databases
Event Place: Berlin, Germany

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Information Marginalization on Subgraphs},
  author = {Huang, J. and Zhu, T. and Rereiner, R. and Zhou, D. and Schuurmans, D.},
  journal = {Knowledge Discovery in Databases: PKDD 2006},
  booktitle = {ECML/PKDD 2006},
  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},
  month_numeric = {9}