A Regularization Framework for Learning from Graph Data
Dengyong Zhou and Bernhard Schölkopf
In: Workshop on Statistical Relational Learning at Twenty-first International Conference on Machine Learning, Canada(2004).
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.