Skip to main content
Article thumbnail
Location of Repository


By Koji Tsuda and Hiroto Saigo


Keywords: Supervised learning on graphs is a central subject in graph data processing. In graph classification and regression, we assume that the target values of a certain number of graphs or a certain part of a graph are available as a training dataset, and our goal is to derive the target values of other graphs or the remaining part of the graph. In drug discovery applications, for example, a graph and its target value correspond to a chemical compound and its chemical activity. In this chapter, we review state-of-the-art methods of graph classification. In particular, we focus on two representative methods, graph kernels and graph boosting, and we present other methods in relation to the two methods. We describe the strengths and weaknesses of different graph classification methods and recent efforts to overcome the challenges. graph classification, graph mining, graph kernels, graph boosting 1.

Publisher: 2013-09-21
Year: 2013
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.