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Review of Graph Invariants for Quantitative Analysis of Structure Dynamics

  • Wojciech Czech
  • Witold Dzwinel
Part of the Studies in Computational Intelligence book series (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.

Keywords

Graphic Processing Unit Cellular Automaton Heat Kernel Vascular Network Betweenness Centrality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakówPoland

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