Faculty of Computing
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4202
Browse
7 results
Filters
Advanced Search
Filter by
Settings
Search Results
Publication Embargo Quantifying encircling behaviour in complex networks(IEEE, 2013-04-16) Piraveenan, M; Uddin, S; Chung, K. S. K; Kasthurirathna, DIn this paper, we explore the effect of encircling behaviour on the topology of complex networks. We introduce the concept of topological encircling, which we define as an attacker making links to neighbours of a victim with the ultimate aim of undermining that victim. We introduce metrics to quantify topological encircling in complex networks, both at the network level and node pair (link) level. Using synthesized networks, we demonstrate that our measures are able to distinguish intentional topological encircling from preferential mixing. We discuss the potential utility of our measures and future research directions.Publication Embargo Standard deviations of degree differences as indicators of mixing patterns in complex networks(IEEE, 2013-08-25) Thedchanamoorthy, G; Piraveenan, M; Kasthurirathna, DMixing 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.Publication Open Access Placement matters in making good decisions sooner: the influence of topology in reaching public utility thresholds(acm.org, 2019-08-27) Kasthurirathna, D; Piraveenan, M; Law, S. Y—Social systems are increasingly being modelled as complex networks, and the interactions and decision making of individuals in such systems can be modelled using game theory. Therefore, networked game theory can be effectively used to model social dynamics. Individuals can use pure or mixed strategies in their decision making, and recent research has shown that there is a connection between the topological placement of an individual within a social network and the best strategy they can choose to maximise their returns. Therefore, if certain individuals have a preference to employ a certain strategy, they can be swapped or moved around within the social network to more desirable topological locations where their chosen strategies will be more effective. To this end, it has been shown that to increase the overall public good, the cooperators should be placed at the hubs, and the defectors should be placed at the peripheral nodes. In this paper, we tackle a related question, which is the time (or number of swaps) it takes for individuals who are randomly placed within the network to move to optimal topological locations which ensure that the public utility satisfies a certain utility threshold. We show that this time depends on the topology of the social network, and we analyse this topological dependence in terms of topological metrics such as scale-free exponent, assortativity, clustering coefficient, and Shannon information content. We show that the higher the scale-free exponent, the quicker the public utility threshold can be reached by swapping individuals from an initial random allocation. On the other hand, we find that assortativity has negative correlation with the time it takes to reach the public utility threshold. We find also that in terms of the correlation between information content and the time it takes to reach a public utility threshold from a random initial assignment, there is a bifurcation: one class of networks show a positive correlation, while another shows a negative correlation. Our results highlight that by designing networks with appropriate topological properties, one can minimise the need for the movement of individuals within a network before a certain public good threshold is achieved. This result has obvious implications for defence strategies in particular.Publication Open Access Quantifying the Robustness of Complex Networks with Heterogeneous Nodes(Multidisciplinary Digital Publishing Institute, 2021-01) Ratnayake, P; Weragoda, S; Wansapura, J; Kasthurirathna, D; Piraveenan, MThe robustness of a complex network measures its ability to withstand random or targeted attacks. Most network robustness measures operate under the assumption that the nodes in a network are homogeneous and abstract. However, most real-world networks consist of nodes that are heterogeneous in nature. In this work, we propose a robustness measure called fitnessincorporated average network efficiency, that attempts to capture the heterogeneity of nodes using the ‘fitness’ of nodes in measuring the robustness of a network. Further, we adopt the same measure to compare the robustness of networks with heterogeneous nodes under varying topologies, such as the scale-free topology or the Erd˝os–Rényi random topology. We apply the proposed robustness measure using a wireless sensor network simulator to show that it can be effectively used to measure the robustness of a network using a topological approach. We also apply the proposed robustness measure to two real-world networks; namely the CO2 exchange network and an air traffic network. We conclude that with the proposed measure, not only the topological structure, but also the fitness function and the fitness distribution among nodes, should be considered in evaluating the robustness of a complex network.Publication Open Access Optimisation of strategy placements for public good in complex networks(acm.org, 2014-08-04) Kasthurirathna, D; Nguyen, H; Piraveenan, M; Uddin, S; Senanayake, UGame theory has long been used to model cognitive decision making in societies. While traditional game theoretic modelling has focussed on well-mixed populations, recent research has suggested that the topological structure of social networks play an important part in the dynamic behaviour of social systems. Any agent or person playing a game employs a strategy (pure or mixed) to optimise pay-off. Previous studies have analysed how selfish agents can optimise their payoffs by choosing particular strategies within a social network model. In this paper we ask the question that, if agents were to work towards the common goal of increasing the public good (that is, the total network utility), what strategies they should adapt within the context of a heterogeneous network. We consider a number of classical and recently demonstrated game theoretic strategies, including cooperation, defection, general cooperation, Pavlov, and zero-determinant strategies, and compare them pairwise. We use the Iterative Prisoners Dilemma game simulated on scale-free networks, and use a genetic-algorithmic approach to investigate what optimal placement patterns evolve in terms of strategy. In particular, we ask the question that, given a pair of strategies are present in a network, which strategy should be adopted by the hubs (highly connected people), for the overall betterment of society (high network utility). We find that cooperation as opposed to defection, Pavlov as opposed to general cooperator, general cooperator as opposed to zero-determinant, and pavlov as opposed to zero-determinant, strategies will be adopted by the hubs, for the overall increased utility of the network. The results are interesting, since given a scenario where certain individuals are only capable of implementing certain strategies, the results give a blueprint on where they should be placed in a complex network for the overall benefit of the society.Publication Open 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, GIn 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.Publication Open Access Influence of topology in the evolution of coordination in complex networks under information diffusion constraints(Springer Berlin Heidelberg, 2014-01-07) Kasthurirathna, D; Piraveenan, M; Harré, MIn this paper, we study the influence of the topological structure of social systems on the evolution of coordination in them. We simulate a coordination game (“Stag-hunt”) on four well-known classes of complex networks commonly used to model social systems, namely scale-free, small-world, random and hierarchical-modular, as well as on the well-mixed model. Our particular focus is on understanding the impact of information diffusion on coordination, and how this impact varies according to the topology of the social system. We demonstrate that while time-lags and noise in the information about relative payoffs affect the emergence of coordination in all social systems, some topologies are markedly more resilient than others to these effects. We also show that, while non-coordination may be a better strategy in a society where people do not have information about the payoffs of others, coordination will quickly emerge as the better strategy when people get this information about others, even with noise and time lags. Societies with the so-called small-world structure are most conducive to the emergence of coordination, despite limitations in information propagation, while societies with scale-free topologies are most sensitive to noise and time-lags in information diffusion. Surprisingly, in all topologies, it is not the highest connected people (hubs), but the slightly less connected people (provincial hubs) who first adopt coordination. Our findings confirm that the evolution of coordination in social systems depends heavily on the underlying social network structure.