Browsing by Author "Piraveenan, M"

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Embargo
Centrality and composition of four-node motifs in metabolic networks
(Elsevier, 2013-01-01) Piraveenan, M; Wimalawarne, K; Kasthurirathn, D
Analysing subgraph patterns and recurring motifs in networks is a useful way to understand their local topology and func- tion. Motifs have been considered useful in analysing design patterns of networks as well. Three-node patterns (triads) in metabolic networks have been studied to some extent producing classification of organisms based on triads, but their network placement was not analysed. We obtain the frequencies of all four-node subgraphs in a wide range of metabolic networks. We construct significance profiles of subgraphs and employ correlation analysis to compare and contrast these profiles, highlight- ing four-node motifs. We then compute specific centrality measures of nodes involved in each subgraph, namely betweenness centrality and closeness centrality. We observe that multiple four-node motifs exist in metabolic networks. The correlation analysis shows that the significance profiles of Eukaryotic networks are highly correlated across organisms, whereas those of the Prokaryotic networks are correlated less so. The centrality indices of nodes that participate in identified network motifs are shown to be quite high. The analysis provides a tool to pinpoint the transition between evolution stages of Prokaryotic and Eukaryotic metabolic networks.
Embargo
Cyclic preferential attachment in complex networks
(Elsevier, 2013-01-01) Kasthurirathna, D; Piraveenan, M
Preferential Attachment (PA), which was originally proposed in the Barabasi-Albert (BA) Model, has been widely ac- cepted as a network growth model which returns in scale-free networks. Preferential attachment in the BA model operates on the assumption that a node which has more links has a better likelihood to create new links. In this work, we expand the PA mechanism by treating it as a cyclic mechanism which is linked to both direct and indirect neighbours of a node. The assumption behind this extension is that the preference of nodes is influenced by their indirect neighbours as well. We show that traditional PA can be absorbed as a special case of this new growth model, which we name ‘cyclic preferential attachment’ (CPA). We also discuss the properties of simulated networks that were generated based on CPA. Finally, we compare and contrast the CPA based networks with the traditional PA based networks and several real-world networks of similar sizes and link-to-node ratios, and show that CPA offers more flexibility in modeling real world networks.
Open Access
Disassortative Mixing and Systemic Rational Behaviour: How System Rationality Is Influenced by Topology and Placement in Networked Systems
(MDPI, 2022-09-12) Kasthurirathna, D; Ratnayake, P; Piraveenan, M
Interdependent decisionmaking of individuals in social systems can be modelled by games played on complex networks. Players in such systems have bounded rationality, which influences the computation of equilibrium solutions. It has been shown that the ‘system rationality’, which indicates the overall rationality of a network of players, may play a key role in the emergence of scale-free or core-periphery topologies in real-world networks. In this work, we identify optimal topologies and mixing patterns of players which can maximise system rationality. Based on simulation results, we show that irrespective of the placement of nodes with higher rationality, it is the disassortative mixing of node rationality that helps to maximize system rationality in a population. In other words, the findings of this work indicate that the overall rationality of a population may improve when more players with non-similar individual rationality levels interact with each other. We identify particular topologies such as the core-periphery topology, which facilitates the optimisation of system rationality. The findings presented in this work may have useful interpretations and applications in socio-economic systems for maximizing the utility of interactions in a population of strategic players.
Open Access
Disassortative mixing of boundedly-rational players in socio-ecological systems
(researchgate.net, 2022-03-25) Ratnayake, P; Kasthurirathna, D; Piraveenan, M
Bounded rationality refers to the non-optimal rationality of players in non-cooperative games. In a networked game, the bounded rationality of players may be heterogeneous and spatially distributed. It has been shown that the ‘system rationality’, which indicates the overall rationality of a network of players, may play a key role in the emergence of scale-free or core-periphery topologies in real-world networks. On the other hand, scalar-assortativity is a metric used to quantify the assortative mixing of nodes with respect to a given scalar attribute. In this work, we observe the effect of node rationality-based scalar-assortativity, on the system rationality of a network. Based on simulation results, we show that irrespective of the placement of nodes with higher rationality, it is the disassortative mixing of node rationality that helps to maximize system rationality in a population. The findings may have useful interpretations and applications in socio-economic systems in maximizing the utility of interactions in a population of strategic players
Open Access
Emergence of scale-free characteristics in socio-ecological systems with bounded rationality
(Nature Publishing Group, 2015-06-11) Kasthurirathna, D; Piraveenan, M
Socio–ecological systems are increasingly modelled by games played on complex networks. While the concept of Nash equilibrium assumes perfect rationality, in reality players display heterogeneous bounded rationality. Here we present a topological model of bounded rationality in socio-ecological systems, using the rationality parameter of the Quantal Response Equilibrium. We argue that system rationality could be measured by the average Kullback–-Leibler divergence between Nash and Quantal Response Equilibria and that the convergence towards Nash equilibria on average corresponds to increased system rationality. Using this model, we show that when a randomly connected socio-ecological system is topologically optimised to converge towards Nash equilibria, scale-free and small world features emerge. Therefore, optimising system rationality is an evolutionary reason for the emergence of scale-free and small-world features in socio-ecological systems. Further, we show that in games where multiple equilibria are possible, the correlation between the scale-freeness of the system and the fraction of links with multiple equilibria goes through a rapid transition when the average system rationality increases. Our results explain the influence of the topological structure of socio–ecological systems in shaping their collective cognitive behaviour and provide an explanation for the prevalence of scale-free and small-world characteristics in such systems.
Embargo
Evolution of coordination in scale-free and small world networks under information diffusion constraints
(IEEE, 2013-08-25) Kasthurirathna, D; Piraveenan, M; Harre, M
We study evolution of coordination in social systems by simulating a coordination game in an ensemble of scale-free and small-world networks and comparing the results. We give particular emphasis to the role information about the pay-offs of neighbours plays in nodes adapting strategies, by limiting this information up to various levels. We find that if nodes have no chance to evolutionarily adapt, then non-coordination is a better strategy, however when nodes adapt based on information of the neighbour payoffs, coordination quickly emerges as the better strategy. We find phase transitions in number of coordinators with respect to the relative pay-off of coordination, and these phase transitions are sharper in small-world networks. We also find that when pay-off information of neighbours is limited, small-world networks are able to better cope with this limitation than scale-free networks. We observe that provincial hubs are the quickest to evolutionarily adapt strategies, in both scale-free and small world networks. Our findings confirm that evolutionary tendencies of coordination heavily depend on network topology.
Open Access
Evolutionary stable strategies in networked games: the influence of topology
(Journal of Artificial Intelligence and Soft Computing Research, 2015-04-01) Kasthurirathna, D; Piraveenan, M; Uddin, S
Evolutionary game theory is used to model the evolution of competing strategies in a population of players. Evolutionary stability of a strategy is a dynamic equilibrium, in which any competing mutated strategy would be wiped out from a population. If a strategy is weak evolutionarily stable, the competing strategy may manage to survive within the network. Understanding the network-related factors that affect the evolutionary stability of a strategy would be critical in making accurate predictions about the behaviour of a strategy in a real-world strategic decision making environment. In this work, we evaluate the effect of network topology on the evolutionary stability of a strategy. We focus on two well-known strategies known as the Zero-determinant strategy and the Pavlov strategy. Zero-determinant strategies have been shown to be evolutionarily unstable in a well-mixed population of players. We identify that the Zero-determinant strategy may survive, and may even dominate in a population of players connected through a non-homogeneous network. We introduce the concept of ‘topological stability’ to denote this phenomenon. We argue that not only the network topology, but also the evolutionary process applied and the initial distribution of strategies are critical in determining the evolutionary stability of strategies. Further, we observe that topological stability could affect other well-known strategies as well, such as the general cooperator strategy and the cooperator strategy. Our observations suggest that the variation of evolutionary stability due to topological stability of strategies may be more prevalent in the social context of strategic evolution, in comparison to the biological context.
Open Access
Exploring emergent topological properties in socio-economic networks through learning heterogeneity
(2025-12-10) Karavita, C; Lyu, Z; Kasthurirathna, D; Piraveenan, M
Understanding how individual learning behavior and structural dynamics interact is essential to modeling emergent phenomena in socio-economic networks. While bounded rationality and network adaptation have been widely studied, the role of heterogeneous learning rates–both at the agent and network levels–remains underexplored. This paper introduces a dual-learning framework that integrates individualized learning rates for agents and a rewiring rate for the network, reflecting real-world cognitive diversity and structural adaptability. Using a simulation model based on the Prisoner’s Dilemma and Quantal Response Equilibrium, we analyze how variations in these learning rates affect the emergence of large-scale network structures. Results show that lower and more homogeneously distributed learning rates promote scale-free networks, while higher or more heterogeneously distributed learning rates lead to the emergence of core-periphery topologies. Key topological metrics–including scale-free exponents, Estrada heterogeneity, and assortativity–reveal that both the speed and variability of learning critically shape system rationality and network architecture. This work provides a unified framework for examining how individual learnability and structural adaptability drive the formation of socio-economic networks with diverse topologies, offering new insights into adaptive behavior, systemic organization, and resilience.
Embargo
The failure tolerance of mechatronic software systems to random and targeted attacks
(American Society of Mechanical Engineers, 2013-08-04) Kasthurirathna, D; Dong, A; Piraveenan, M; Tumer, I. Y
This paper describes a complex networks approach to study the failure tolerance of mechatronic software systems under various types of hardware and/or software failures. We produce synthetic system architectures based on evidence of modular and hierarchical modular product architectures and known motifs for the interconnection of physical components to software. The system architectures are then subject to various forms of attack. The attacks simulate failure of critical hardware or software. Four types of attack are investigated: degree centrality, betweenness centrality, closeness centrality and random attack. Failure tolerance of the system is measured by a ‘robustness coefficient’, a topological ‘size’ metric of the connectedness of the attacked network. We find that the betweenness centrality attack results in the most significant reduction in the robustness coefficient, confirming betweenness centrality, rather than the number of connections (i.e. degree), as the most conservative metric of component importance. A counter-intuitive finding is that “designed” system architectures, including a bus, ring, and star architecture, are not significantly more failure-tolerant than interconnections with no prescribed architecture, that is, a random architecture. Our research provides a data-driven approach to engineer the architecture of mechatronic software systems for failure tolerance.
Open Access
Influence modelling using bounded rationality in social networks
(acm.org, 2015-08-25) Kasthurirathna, D; Harre, M; Piraveenan, M
Influence models enable the modelling of the spread of ideas, opinions and behaviours in social networks. Bounded rationality in social network suggests that players make non optimum decisions due to the limitations of access to information. Based on the premise that adopting a state or an idea can be regarded as being 'rational', we propose an influence model based on the heterogeneous bounded rationality of players in a social network. We employ the quantal response equilibrium model to incorporate the bounded rationality in the context of social influence. The bounded rationality of following a seed or adopting the strategy of a seed would be negatively proportional to the distance from that node. This indicates that the closeness centrality would be the appropriate measure to place influencers in a social network. We argue that this model can be used in scenarios where there are multiple types of influencers and varying payoffs of adopting a state. We compare different seed placement mechanisms to compare and contrast the optimum method to minimise the existing social influence in a network when there are multiple and conflicting seeds. We ascertain that placing of opposing seeds according to a measure derived from a combination of the betweenness centrality values from the seeds and the closeness centrality of the network would provide the maximum negative influence.
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, M
In 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.
Embargo
Information Theoretic Approach for Modeling Bounded Rationality in Networked Games
(IEEE, 2019-12-06) Gunawardana, L; Ratnayake, p; Piraveenan, M; Kasthurirathna, D
Bounded rationality of networked interactions lead to non-optimal equilibria. The rationality of a self-interested player is determined by the incoming information from the opponents on their strategies and pay-offs. In this work, we attempt to model the heterogeneously distributed bounded rationality of networked players using the directed information flow, measured using the transfer entropy. In order to compute the non optimal equilibrium, we use the Quantal Response Equilibrium (QRE) model that entails a rationality parameter, which we define as a function of transfer entropy. We then compute the average divergence of the network of strategic interactions from that of the Nash Equilibrium, which we term as the `system rationality', in order to compare and contrast the varying network topologies on their influence on the rationality of players. We observe that the networks demonstrate higher system rationality when the rationality values of players are derived from on the average information flow from neighboring nodes, compared to when the rationality is computed based on the specific information flow from each opponent. Further, we observe that the scale-free and hub-and-spoke topologies lead to more rational interactions compared to random networks, when the rationalities of the interactions are computed based on the average incoming information flow to each node. This may suggest that the networks observed in the real-world may adopt scale-free and hub-and-spoke topologies, in order to facilitate more rational interactions among networks of strategic players.
Embargo
Modeling networked systems using the topologically distributed bounded rationality framework
(Wiley Online Library, 2016-11) Kasthurirathna, D; Piraveenan, M; Uddin, S
In networked systems research, game theory is increasingly used to model a number of scenarios where distributed decision making takes place in a competitive environment. These scenarios include peer-to-peer network formation and routing, computer security level allocation, and TCP congestion control. It has been shown, however, that such modeling has met with limited success in capturing the real-world behavior of computing systems. One of the main reasons for this drawback is that, whereas classical game theory assumes perfect rationality of players, real world entities in such settings have limited information, and cognitive ability which hinders their decision making. Meanwhile, new bounded rationality models have been proposed in networked game theory which take into account the topology of the network. In this article, we demonstrate that game-theoretic modeling of computing systems would be much more accurate if a topologically distributed bounded rationality model is used. In particular, we consider (a) link formation on peer-to-peer overlay networks (b) assigning security levels to computers in computer networks (c) routing in peer-to-peer overlay networks, and show that in each of these scenarios, the accuracy of the modeling improves very significantly when topological models of bounded rationality are applied in the modeling process. Our results indicate that it is possible to use game theory to model competitive scenarios in networked systems in a way that closely reflects real world behavior, topology, and dynamics of such systems. © 2016 Wiley Periodicals, Inc. Complexity 21: 123-137, 2016
Embargo
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.
Open 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.
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, 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.
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, U
Game 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.
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Optimising influence in social networks using bounded rationality models
(Springer Vienna, 2016-12) Kasthurirathna, D; Harre, M; Piraveenan, M
Influence models enable the modelling of the spread of ideas, opinions and behaviours in social networks. Bounded rationality in social networks suggests that players make non-optimum decisions due to the limitations of access to information. Based on the premise that adopting a state or an idea can be regarded as being ‘rational’, we propose an influence model based on the heterogeneous bounded rationality of players in a social network. We employ the quantal response equilibrium model to incorporate the bounded rationality in the context of social influence. We hypothesise that bounded rationality of following a seed or adopting the strategy of a seed is negatively proportional to the distance from that node, and it follows that closeness centrality is the appropriate measure to place influencers in a social network. We argue that this model can be used in scenarios where there are multiple types of influencers and varying pay-offs of adopting a state. We compare different seed placement mechanisms to compare and contrast the optimum method to minimise the existing social influence in a network when there are multiple and conflicting seeds. We ascertain that placing of opposing seeds according to a measure derived from a combination of the betweenness centrality values from the seeds, and the closeness centrality of the network provide the maximum negative influence. Further, we extend this model to a strategic decision-making scenario where each seed operates a strategy in a strategic game.
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Overlay community detection using community networks
(IEEE, 2018-11-18) Bandara, M; Weragoda, S; Piraveenan, M; Kasthurirthna, D
Community detection is useful in understanding the structure of a social network. One of the most commonly used algorithms for community detection is the Louvain algorithm, which is based on the Newman-Girman (NG) modularity optimization technique. It is argued that the close spatial proximity of nodes may increase their chance of being in the same community. Variants of the NG modularity measure such as the dist-modularity attempt to normalize the effect of spatial proximity in extracting communities, causing loss of information about the spatially proximate communities. Other variants of NG modularity such as Spatially-near modularity, try to exploit the spatial proximity of nodes to extract communities, causing loss of information on spatially dispersed communities. We propose that `overlay communities' on existing `community networks' can be used to identify spatially dispersed communities, while preserving the information of spatial proximate communities. The community network is formed by reducing a community into a node using a proximity dimension, which are connected by intercommunity links. The overlay communities are the community pairs that have relatively high normalized link strengths, while being relatively apart in selected proximity dimension. We apply this method to the Gowalla and soc-Pokec online social networks and extract the spatially dispersed overlay communities in them. We select the geographical space and the age of the nodes as the proximity dimension of these two networks, respectively. Detecting spatially dispersed overlay communities may be useful in application domains such as indirect marketing, social engineering, counter terrorism and defense.
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The performance of page rank algorithm under degree preserving perturbations
(IEEE, 2014-12-09) Senanayake, U; Szot, P; Piraveenan, M; Kasthurirathna, D
Page rank is a ranking algorithm based on a random surfer model which is used in Google search engine and many other domains. Because of its initial success in Google search engine, page rank has become the de-facto choice when it comes to ranking nodes in a network structure. Despite the ubiquitous utility of the algorithm, little is known about the effect of topology on the performance of the page rank algorithm. Hence this paper discusses the performance of page rank algorithm under different topological conditions. We use scale-free networks and random networks along with a custom search engine we implemented in order to experimentally prove that the performance of page rank algorithm is deteriorated when the random network is perturbed. In contrast, scale-free topology is proven to be resilient against degree preserving perturbations which aids the page rank algorithm to deliver consistent results across multiple networks that are perturbed to varying proportions. Not only does the top ranking results emerge as stable nodes, but the overall performance of the algorithm is proven to be remarkably resilient which deepens our understanding about the risks in applying page rank algorithm without an initial analysis on the underlying network structure. The results conclusively suggests that while page rank algorithm can be applied to scale-free networks with relatively low risk, applying page rank algorithm to other topologies can be risky as well as misleading. Therefore, the success of the page rank algorithm in real world in search engines such as Google is at least partly due to the fact that the world wide web is a scale-free network. Since the world wide web is constantly evolving, we postulate that if the topological structure of the world wide web changes significantly so that it loses its scale-free nature to some extent, the page rank algorithm will not be as effective.