Generalized graph entropies

Authors

  • Matthias Dehmer,

    Corresponding author
    1. Department for Biomedical Informatics and Mechatronics, Institute for Bioinformatics and Translational Research, UMIT, Eduard Wallnoefer Zentrum 1, A-6060, Hall in Tyrol, Austria
    • Department for Biomedical Informatics and Mechatronics, Institute for Bioinformatics and Translational Research, UMIT, Eduard Wallnoefer Zentrum 1, A-6060, Hall in Tyrol, Austria
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  • Abbe Mowshowitz

    1. Department of Computer Science, The City College of New York (CUNY), 138th Street at Convent Avenue, New York, New York 10031
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Abstract

This article deals with generalized entropies for graphs. These entropies result from applying information measures to a graph using various schemes for defining probability distributions over the elements (e.g., vertices) of the graph. We introduce a new class of generalized measures, develop their properties, compute the measures for selected graphs, and briefly discuss potential applications to classification and clustering problems. © 2011 Wiley Periodicals, Inc. Complexity, 17,45–50, 2011

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