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Distributed flow optimization and cascading effects in weighted complex networks

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Abstract

We investigate the effect of a specific edge weighting scheme ~(k i k j )β on distributed flow efficiency and robustness to cascading failures in scale-free networks. In particular, we analyze a simple, yet fundamental distributed flow model: current flow in random resistor networks. By the tuning of control parameter β and by considering two general cases of relative node processing capabilities as well as the effect of bandwidth, we explore the dependence of transport efficiency upon the correlations between the topology and weights. By studying the severity of cascades for different control parameter β, we find that network resilience to cascading overloads and network throughput is optimal for the same value of β over the range of node capacities and available bandwidth.

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Authors and Affiliations

  1. Social and Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, 110 8th Street, 12180–3590, Troy, New York, USA

    A. Asztalos, S. Sreenivasan, B.K. Szymanski & G. Korniss

  2. Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, 12180–3590, Troy, New York, USA

    A. Asztalos, S. Sreenivasan & B.K. Szymanski

  3. Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute, 110 8th Street, 12180–3590, Troy, New York, USA

    A. Asztalos, S. Sreenivasan & G. Korniss

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  1. A. Asztalos
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  2. S. Sreenivasan
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  4. G. Korniss
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Correspondence to A. Asztalos.

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Asztalos, A., Sreenivasan, S., Szymanski, B. et al. Distributed flow optimization and cascading effects in weighted complex networks. Eur. Phys. J. B 85, 288 (2012). https://doi.org/10.1140/epjb/e2012-30122-3

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  • DOI: https://doi.org/10.1140/epjb/e2012-30122-3

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