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Calibri 83ffff̙̙3f3fff3f3f33333f33333.9TU Delft Repositoryg Xuuidrepository linktitleauthorcontributorpublication yearabstract
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departmentresearch group programmeprojectcoordinates)uuid:5c80616a87ba478d842845f807f473f3Dhttp://resolver.tudelft.nl/uuid:5c80616a87ba478d842845f807f473f3GA network approach for power grid robustness against cascading failures/Wang, X.; Koc, Y.; Kooij, R.E.; Van Mieghem, P.Cascading failures are one of the main reasons for blackouts in electrical power grids. Stable power supply requires a robust design of the power grid topology. Currently, the impact of the grid structure on the grid robustness is mainly assessed by purely topological metrics, that fail to capture the fundamental properties of the electrical power grids such as power flow allocation according to Kirchhoff s laws. This paper deploys the effective graph resistance as a metric to relate the topology of a grid to its robustness against cascading failures. Specifically, the effective graph resistance is deployed as a metric for network expansions (by means of transmission line additions) of an existing power grid. Four strategies based on network properties are investigated to optimize the effective graph resistance, accordingly to improve the robustness, of a given power grid at a low computational complexity. Experimental results suggest the existence of Braess s paradox in power grids: bringing an additional line into the system occasionally results in decrease of the grid robustness. This paper further investigates the impact of the topology on the Braess s paradox, and identifies specific substructures whose existence results in Braess s paradox in power grids. Careful assessment of the design and expansion choices of grid topologies incorporating the insights provided by this paper optimizes the robustness of a power grid, while avoiding the Braess s paradox in the system.enconference paperRNDM8Electrical Engineering, Mathematics and Computer Science"Network Architectures and Services)uuid:5ef0360390454bdfa1db4842f2f82defDhttp://resolver.tudelft.nl/uuid:5ef0360390454bdfa1db4842f2f82defEDo greedy assortativity optimization algorithms produce good results?HWinterbach, W.; De Ridder, D.; Wang, H.J.; Reinders, M.; Van Mieghem, P.EDP sciences.Network Architectures and Services (NAS) Group)uuid:184d12542bb24205818e7824de728c59Dhttp://resolver.tudelft.nl/uuid:184d12542bb24205818e7824de728c59HCorrelating the topology of a metabolic network with its growth capacityFWinterbach, W.; Wang, H.; Reinders, M.; Van Mieghem, P.; De Ridder, D.ICST&Network Architectures & Services (NAS))uuid:a5e4ab47eed44180b346da086e826a02Dhttp://resolver.tudelft.nl/uuid:a5e4ab47eed44180b346da086e826a02>Metabolic network destruction: Relating topology to robustnessBiological networks exhibit intriguing topological properties such as smallworldness. In this paper, we investigate whether the topology of a metabolic network is related to its robustness. We do so by perturbing a metabolic system in silico, one reaction at a time and studying the correlations between growth, as predicted by flux balance analysis, and a number of topological metrics, as computed from three network representations of the metabolic system. We find that a small number of metrics correlate with growth and that only one of the network representations stands out in terms of correlated metrics. The most correlated metrics point to the importance of hub nodes in this network: socalled "currency metabolites". Since they are responsible for interconnecting distant functional modules in the network, they are important points in the networks for predicting if reaction removal affects growth. Source code and data are available upon request.Emetabolic networks; ux balance analysis; network topology; robustness)uuid:ff66e490db594e3cb6e2926da4f074dfDhttp://resolver.tudelft.nl/uuid:ff66e490db594e3cb6e2926da4f074df5Algebraic Connectivity Optimizatio<n via Link AdditionWang, H.; Van Mieghem, P.Dalgebraic connectivity; synchronization; optimization; link addition)uuid:9426fb39a92e41e09a825075d08a7b35Dhttp://resolver.tudelft.nl/uuid:9426fb39a92e41e09a825075d08a7b35YThe Effect of Peer Selection with Hopcount or Delay Constraint on PeertoPeer Networking#Tang, S.; Wang, H.; Van Mieghem, P.)uuid:abb66a4a4d0846529f16ae697c85f6cfDhttp://resolver.tudelft.nl/uuid:abb66a4a4d0846529f16ae697c85f6cf%Shifting the Link Weights in Networks)uuid:ee8d18fbb8464009baa267a65ffc32d3Dhttp://resolver.tudelft.nl/uuid:ee8d18fbb8464009baa267a65ffc32d30A Qualitative Comparison of Power Law Generators=Martin Hernandez, J.; Kleiberg, T.; Wang, H.; Van Mieghem, P.9network topology; internet; power law; graphs; algorithms)uuid:50689b82502c49b6b265311a592f1642Dhttp://resolver.tudelft.nl/uuid:50689b82502c49b6b265311a592f16427Constructing the Overlay Network by Tuning Link Weights)uuid:357b3fb58dfc4d75973b4c1b6dc2d49dDhttp://resolver.tudelft.nl/uuid:357b3fb58dfc4d75973b4c1b6dc2d49d<Topological Characteristics of the Dutch Road Infrastructure(Jamakovic, A.; Wang, H.; Van Mieghem, P.)uuid:2b1acb2ad4db40ab8cb3d57033a47e57Dhttp://resolver.tudelft.nl/uuid:2b1acb2ad4db40ab8cb3d57033a47e57+The Stability of Paths in a Dynamic Network(Kuipers, F.A.; Wang, H.; Van Mieghem, P.gnetwork dynamics; linkstate update policy; shortest path; link weight perturbation; quality of service)uuid:055f7afd22bf4bc5b841dddf31b66217Dhttp://resolver.tudelft.nl/uuid:055f7afd22bf4bc5b841dddf31b662175Degree and Principal Eigenvectors in Complex Networks!Li, C.; Wang, H.; Van Mieghem, P.The largest eigenvalue ? 1 of the adjacency matrix powerfully characterizes dynamic processes on networks, such as virus spread and synchronization. The minimization of the spectral radius by removing a set of links (or nodes) has been shown to be an NPcomplete problem. So far, the best heuristic strategy is to remove links/nodes based on the principal eigenvector corresponding to the largest eigenvalue ? 1. This motivates us to investigate properties of the principal eigenvector x 1 and its relation with the degree vector. (a) We illustrate and explain why the average E[x 1] decreases with the linear degree correlation coefficient ? D in a network with a given degree vector; (b) The difference between the principal eigenvector and the scaled degree vector is proved to be the smallest, when ?1=N2N1 , where N k is the total number walks in the network with k hops; (c) The correlation between the principal eigenvector and the degree vector decreases when the degree correlation ? D is decreased.Hnetworks; spectral radius; principal eigenvector; degree; assortativitySpringer.Network Architectures and Services Group (NAS)
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