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Advances in Intelligent Modelling and Simulation pp 325–343Cite as

Review of Graph Invariants for Quantitative Analysis of Structure Dynamics

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 416))

Abstract

In this work we review graph invariants used for quantitative analysis of evolving graphs. Focusing on graph datasets derived from structural pattern recognition and complex networks fields, we demonstrate how to capture relevant topological features of networks. In an experimental setup, we study structural properties of graphs representing rotating 3D objects and show how they are related to characteritics of undelying images. We present how evolving strucure of Autonomous Systems (ASs) network is reflected by non-trivial changes in scalar graph descriptors. We also inspect characteristics of growing tumor vascular networks, obtained from a simulation. Additionally, the overview of currently used graph invariants with several possible groupings is provided.

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Author information

Authors and Affiliations

  1. AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Wojciech Czech & Witold Dzwinel

Authors
  1. Wojciech Czech
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  2. Witold Dzwinel
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Correspondence to Wojciech Czech .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, Kraków, 30-059, Poland

    Aleksander Byrski

  2. Faculty of Applied Informatics, Tomas Bata University, Nam. T.G.Masaryka 5555, Zlín, 76001, Czech Republic

    Zuzana Oplatková

  3. Florida Institute of Technology, 150 W. University Blvd., Melbourne, 32901, Florida, USA

    Marco Carvalho

  4. Dept. of Computer Science, AGH University of Science and Technology, Al. Mickiewicza, Kraków, 30-059, Poland

    Marek Kisiel-Dorohinicki

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Czech, W., Dzwinel, W. (2012). Review of Graph Invariants for Quantitative Analysis of Structure Dynamics. In: Byrski, A., Oplatková, Z., Carvalho, M., Kisiel-Dorohinicki, M. (eds) Advances in Intelligent Modelling and Simulation. Studies in Computational Intelligence, vol 416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28888-3_13

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  • DOI: https://doi.org/10.1007/978-3-642-28888-3_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28887-6

  • Online ISBN: 978-3-642-28888-3

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