Faculty of Computing

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Author: search.filters.author.Thedchanamoorthy, G

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Now showing 1 - 4 of 4
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    PublicationEmbargo
    Standard deviations of degree differences as indicators of mixing patterns in complex networks
    (IEEE, 2013-08-25) Thedchanamoorthy, G; Piraveenan, M; Kasthurirathna, D
    Mixing patterns in social networks can give us important clues about the structure and functionality of these networks. In the past, a number of measures including variants of assortativity have been used to quantify degree mixing patterns of networks. In this paper, we are interested in observing the heterogeneity of the neighbourhood of nodes in networks. For this purpose, we use the standard deviation of degree differences between a node and its neighbours. We call this measure the `versatility' of a node. We apply this measure on synthetic and real world networks. We find that among real world networks three classes emerge -(i) Networks where the versatility converges to non-zero values with node degree (ii) Networks where the versatility converges to zero with node degree (iii) Networks where versatility does not converge with node degree. We find that there may be some correlation between this and network density, and the geographical / anatomical nature of networks may also be a factor. We also note that versatility could be applicable to any quantifiable network property, and not just node degree.
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    PublicationEmbargo
    Network robustness and topological characteristics in scale-free networks
    (IEEE, 2013-04-16) Kasthurirathna, D; Piraveenan, M; Thedchanamoorthy, G
    In this paper, we explore the relationship between the topological characteristics of a complex network and its robustness to sustained targeted attacks. Using synthesized scale-free networks, we look at a number of network measures, including rich club profiles, scale-free exponent, modularity, assortativity, average path length and clustering coefficient of a network, and how each of these influence the robustness of a scale-free network under targeted attacks. We consider sustained targeted attacks by order of node degree. We show that assortativity 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 the modularity and robustness, scale-free exponent and robustness, or rich-club profiles and robustness. 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 networks under sustained targeted attacks.
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    PublicationOpen Access
    On the influence of topological characteristics on robustness of complex networks
    (Journal of Artificial Intelligence and Soft Computing Research, 2013) Kasthurirathna, D; Piraveenan, M; Thedchanamoorthy, G
    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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    PublicationOpen Access
    Node assortativity in complex networks: An alternative approach
    (Elsevier, 2014-01-01) Thedchanamoorthy, G; Piraveenan, M; Kasthuriratna, D; Senanayake, U
    Assortativity quantifies the tendency of nodes being connected to similar nodes in a complex network. Degree Assortativity can be quantified as a Pearson correlation. However, it is insufficient to explain assortative or disassortative tendencies of individual nodes or links, which may be contrary to the overall tendency of the network. A number of ‘local’ assortativity measures have been proposed to address this. In this paper we define and analyse an alternative formulation for node assortativity, primarily for undirected networks. The alternative approach is justified by some inherent shortcomings of existing local measures of assortativity. Using this approach, we show that most real world scale-free networks have disassortative hubs, though we can synthesise model networks which have assortative hubs. Highlighting the relationship between assortativity of the hubs and network robustness, we show that real world networks do display assortative hubs in some instances, particularly when high robustness to targeted attacks is a necessity.