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A Regularization Framework for Learningfrom Graph Data

2004

Conference Paper

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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.

Author(s): Zhou, D. and Schölkopf, B.
Book Title: ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields
Pages: 132-137
Year: 2004
Day: 0

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

Event Name: ICML 2004

Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2688,
  title = {A Regularization Framework for Learningfrom Graph Data},
  author = {Zhou, D. and Sch{\"o}lkopf, B.},
  booktitle = {ICML  Workshop on Statistical Relational Learning and Its Connections to Other Fields},
  pages = {132-137},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2004}
}