@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},
  author = {Zhou, D. and Sch{\"o}lkopf, B.}
}