Browsing by Author "Moutsinas, Giannis"

Now showing 1 - 4 of 4
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    Graph hierarchy: a novel framework to analyse hierarchical structures in complex networks
    (Nature Publishing Group, 2021-07-06) Moutsinas, Giannis; Shuaib, Choudhry; Guo, Weisi; Jarvis, Stephen
    Trophic coherence, a measure of a graph’s hierarchical organisation, has been shown to be linked to a graph’s structural and dynamical aspects such as cyclicity, stability and normality. Trophic levels of vertices can reveal their functional properties, partition and rank the vertices accordingly. Trophic levels and hence trophic coherence can only be defined on graphs with basal vertices, i.e. vertices with zero in-degree. Consequently, trophic analysis of graphs had been restricted until now. In this paper we introduce a hierarchical framework which can be defined on any simple graph. Within this general framework, we develop several metrics: hierarchical levels, a generalisation of the notion of trophic levels, influence centrality, a measure of a vertex’s ability to influence dynamics, and democracy coefficient, a measure of overall feedback in the system. We discuss how our generalisation relates to previous attempts and what new insights are illuminated on the topological and dynamical aspects of graphs. Finally, we show how the hierarchical structure of a network relates to the incidence rate in a SIS epidemic model and the economic insights we can gain through it.
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    Node-level resilience loss in dynamic complex networks
    (Nature Publishing Group, 2020-02-27) Moutsinas, Giannis; Guo, Weisi
    In an increasingly connected world, the resilience of networked dynamical systems is important in the fields of ecology, economics, critical infrastructures, and organizational behaviour. Whilst we understand small-scale resilience well, our understanding of large-scale networked resilience is limited. Recent research in predicting the effective network-level resilience pattern has advanced our understanding of the coupling relationship between topology and dynamics. However, a method to estimate the resilience of an individual node within an arbitrarily large complex network governed by non-linear dynamics is still lacking. Here, we develop a sequential mean-field approach and show that after 1-3 steps of estimation, the node-level resilience function can be represented with up to 98% accuracy. This new understanding compresses the higher dimensional relationship into a one-dimensional dynamic for tractable understanding, mapping the relationship between local dynamics and the statistical properties of network topology. By applying this framework to case studies in ecology and biology, we are able to not only understand the general resilience pattern of the network, but also identify the nodes at the greatest risk of failure and predict the impact of perturbations. These findings not only shed new light on the causes of resilience loss from cascade effects in networked systems, but the identification capability could also be used to prioritize protection, quantify risk, and inform the design of new system architectures.
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    Probabilistic stability of traffic load balancing on wireless complex networks
    (IEEE, 2019-12-12) Moutsinas, Giannis; Guo, Weisi
    Load balancing between adjacent base stations (BSs) is important for balancing load distributions and improving service provisioning. While load balancing between any given pair of BSs is beneficial, cascade load sharing can cause network-level instability that is hard to predict. The relationship between each BS's load balancing dynamics and the network topology is not understood. In this seminal work on stability analysis, we consider a frequency reuse network with no interference, whereby load balancing dynamics does not perturb the individual cells' capacity. Our novelty is to show an exact analytical and also a probabilistic relationship for stability, relating generalized local load balancing dynamics with a generalized network topology, as well as the uncertainty we have in load balancing parameters due to noisy channel or network sensing. We prove that the stability analysis is valid for any generalized load balancing dynamics and topological cell deployment, and we believe this general relationship can inform the joint design of both the load balancing dynamics and the neighbor list of the network. The probabilistic framework provides uncertainty quantification and stability prediction for Digital Twins of wireless infrastructure
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    Uncertainty of resilience in complex networks with nonlinear dynamics
    (IEEE, 2020-11-24) Moutsinas, Giannis; Zou, Mengbang; Guo, Weisi
    Resilience is a system’s ability to maintain its function when perturbations and errors occur. Whilst we understand low-dimensional networked systems’s behavior well, our understanding of systems consisting of a large number of components is limited. Recent research in predicting the network level resilience pattern has advanced our understanding of the coupling relationship between global network topology and local nonlinear component dynamics. However, when there is uncertainty in the model parameters, our understanding of how this translates to uncertainty in resilience is unclear for a large-scale networked system. Here we develop a polynomial chaos expansion method to estimate the resilience for a wide range of uncertainty distributions. By applying this method to case studies, we not only reveal the general resilience distribution with respect to the topology and dynamics submodels but also identify critical aspects to inform better monitoring to reduce uncertainty.