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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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On the Influence of Topological Characteristics on Robustness of Complex Networks

Dharshana Kasthurirathna
  • Thedchanamoorthy Centre for Complex Systems Research Faculty of Engineering and IT The University of Sydney, NSW 2006, Australia
/ Mahendra Piraveenan
  • Centre for Complex Systems Research Faculty of Engineering and IT The University of Sydney, NSW 2006, Australia
/ Gnanakumar Thedchanamoorthy
  • Centre for Complex Systems Research Faculty of Engineering and IT The University of Sydney, NSW 2006, Australia
Published Online: 2014-12-30 | DOI: https://doi.org/10.2478/jaiscr-2014-0007


In this paper, we explore the relationship between the topological characteristics of a complex network and its robustness to sustained targeted attacks. Using synthesised scale-free, small-world and random networks, we look at a number of network measures, including assortativity, modularity, average path length, clustering coefficient, rich club profiles and scale-free exponent (where applicable) of a network, and how each of these influence the robustness of a network under targeted attacks. We use an established robustness coefficient to measure topological robustness, and consider sustained targeted attacks by order of node degree. With respect to scale-free networks, we show that assortativity, modularity and average path length have a positive correlation with network robustness, whereas clustering coefficient has a negative correlation. We did not find any correlation between scale-free exponent and robustness, or rich-club profiles and robustness. The robustness of small-world networks on the other hand, show substantial positive correlations with assortativity, modularity, clustering coefficient and average path length. In comparison, the robustness of Erdos-Renyi random networks did not have any significant correlation with any of the network properties considered. A significant observation is that high clustering decreases topological robustness in scale-free networks, yet it increases topological robustness in small-world networks. Our results highlight the importance of topological characteristics in influencing network robustness, and illustrate design strategies network designers can use to increase the robustness of scale-free and small-world networks under sustained targeted attacks.


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About the article

Published Online: 2014-12-30

Published in Print: 2013-04-01

Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.2478/jaiscr-2014-0007.

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