Faculty of Computing

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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.
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
Embargo
Quantifying encircling behaviour in complex networks
(IEEE, 2013-04-16) Piraveenan, M; Uddin, S; Chung, K. S. K; Kasthurirathna, D
In 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.
Embargo
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.
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.
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.
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, M
The 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.
Embargo
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.
Embargo
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.

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