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Graph Classification

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Managing and Mining Graph Data

Part of the book series: Advances in Database Systems ((ADBS,volume 40))

Abstract

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.

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Author information

Authors and Affiliations

  1. Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

    Koji Tsuda

  2. Max Planck Institute for Informatics, Saarbru̶cken, Germany

    Hiroto Saigo

Authors
  1. Koji Tsuda
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  2. Hiroto Saigo
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Correspondence to Koji Tsuda .

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Editors and Affiliations

  1. Thomas J. Watson Research Center, IBM, Skyline Drive 19, Hawthorne, 10532, U.S.A.

    Charu C. Aggarwal

  2. Microsoft Research Asia, Zhichun Road 49, Beijing, 100080, China, People's Republic

    Haixun Wang

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Tsuda, K., Saigo, H. (2010). Graph Classification. In: Aggarwal, C., Wang, H. (eds) Managing and Mining Graph Data. Advances in Database Systems, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6045-0_11

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  • DOI: https://doi.org/10.1007/978-1-4419-6045-0_11

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  • Print ISBN: 978-1-4419-6044-3

  • Online ISBN: 978-1-4419-6045-0

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