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 |
| Bibtex Type: | Conference Paper (inproceedings) |
| Event Name: | ICML 2004 |
| Electronic Archiving: | grant_archive |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{2688,
title = {A Regularization Framework for Learningfrom Graph Data},
booktitle = {ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields},
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.},
pages = {132-137},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
year = {2004},
slug = {2688},
author = {Zhou, D. and Sch{\"o}lkopf, B.}
}