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Information Theoretic Prototype Selection for Unattributed Graphs

  • Lin Han
  • Luca Rossi
  • Andrea Torsello
  • Richard C. Wilson
  • Edwin R. Hancock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)

Abstract

In this paper we propose a prototype size selection method for a set of sample graphs. Our first contribution is to show how approximate set coding can be extended from the vector to graph domain. With this framework to hand we show how prototype selection can be posed as optimizing the mutual information between two partitioned sets of sample graphs. We show how the resulting method can be used for prototype graph size selection. In our experiments, we apply our method to a real-world dataset and investigate its performance on prototype size selection tasks.

Keywords

Prototype Selection Mutual information Importance Sampling Partition function 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lin Han
    • 1
  • Luca Rossi
    • 2
  • Andrea Torsello
    • 2
  • Richard C. Wilson
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkUK
  2. 2.Department of Environmental Science, Informatics and StatisticsCa’ Foscari Univerisity of VeniceItaly

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