Research Papers - Dept of Software Engineering

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/1022

Browse

Filters

2013 - 201921

Advanced Search

Filter by

Settings

Author: search.filters.author.Piraveenan, MEnd date: 2019

Search Results

Now showing 1 - 10 of 21
  • Thumbnail Image
    PublicationEmbargo
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    PublicationOpen 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.
  • Thumbnail Image
    PublicationEmbargo
    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.
  • Thumbnail Image
    PublicationEmbargo
    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.
  • Thumbnail Image
    PublicationEmbargo
    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.
  • Thumbnail Image
    PublicationEmbargo
    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.
  • Thumbnail Image
    PublicationEmbargo
    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.
  • Thumbnail Image
    PublicationEmbargo
    Topological stability of evolutionarily unstable strategies
    (IEEE, 2014-12-09) Kasthurirathna, D; Piraveenan, M
    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.
  • Thumbnail Image
    PublicationOpen 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.