Distributed Link Removal Using Local Estimation of Network Topology

Gusrialdi, Azwirman; Qu, Zhihua; Hirche, Sandra

doi:10.1109/TNSE.2018.2813426

Title: Distributed Link Removal Using Local Estimation of Network Topology

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Abstract

This paper considers the problem of network structure manipulation in the absence of information on the global network topology. In particular, the problem of removing some of links is investigated in order to slow or stop the spread of disease in a network while preserving its connectivity. Existing methods solve this combinatorial problem in a centralized manner and they require the global information of network structure. In this paper, we propose a distributed design algorithm to compute a suboptimal solution to this problem efficiently by mimicking gradient based method, namely by iteratively removing one or multiple links at a time from the network. Specifically, using matrix perturbation analysis we formulate an optimization problem involving the eigenvector associated with the largest eigenvalue of the adjacency matrix and whose solution is equal to a suboptimal solution to the original problem. This strategy also enables us to overcome the combinatorial issue of the problem. Distributed algorithms to estimate the eigenvector and to verify network's connectivity are then proposed which facilitate us to solve the new optimization problem. In addition, topological insights into the proposed algorithm and optimality of its solution are also discussed. Finally, the proposed distributed design method is demonstrated and evaluatedmore » via several numerical examples.« less

Authors:

^[1];

^[2]

Univ. of Central Florida, Orlando, FL (United States)
Technische Universität München, Munich, Bavaria (Germany)

Publication Date:: Mon Mar 12 00:00:00 EDT 2018

Research Org.:: Univ. of Central Florida, Orlando, FL (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Science Foundation (NSF); US Department of Transportation; L-3 Communication; Leidos

OSTI Identifier:: 1601126

Alternate Identifier(s):: OSTI ID: 1820538

Grant/Contract Number:: EE0007327; EE0006340; ECCS-1308928; CCF-0956501; DTRT13-G-UTC51; 11013I2034; P010161530; EE0007998

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Network Science and Engineering

Additional Journal Information:: Journal Volume: 6; Journal Issue: 3; Journal ID: ISSN 2334-329X

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Link removal; largest eigenvalue minimization; distributed algorithm; matrix perturbation; 24 POWER TRANSMISSION AND DISTRIBUTION

Citation Formats


                    Gusrialdi, Azwirman, Qu, Zhihua, and Hirche, Sandra. Distributed Link Removal Using Local Estimation of Network Topology.  United States: N. p., 2018. 
Web.  doi:10.1109/TNSE.2018.2813426.

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                    Gusrialdi, Azwirman, Qu, Zhihua, & Hirche, Sandra. Distributed Link Removal Using Local Estimation of Network Topology.  United States.  https://doi.org/10.1109/TNSE.2018.2813426

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                    Gusrialdi, Azwirman, Qu, Zhihua, and Hirche, Sandra. Mon .  
"Distributed Link Removal Using Local Estimation of Network Topology".  United States.  https://doi.org/10.1109/TNSE.2018.2813426.  https://www.osti.gov/servlets/purl/1601126.

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@article{osti_1601126,

  title        = {Distributed Link Removal Using Local Estimation of Network Topology},

  author       = {Gusrialdi, Azwirman and Qu, Zhihua and Hirche, Sandra},

  abstractNote = {This paper considers the problem of network structure manipulation in the absence of information on the global network topology. In particular, the problem of removing some of links is investigated in order to slow or stop the spread of disease in a network while preserving its connectivity. Existing methods solve this combinatorial problem in a centralized manner and they require the global information of network structure. In this paper, we propose a distributed design algorithm to compute a suboptimal solution to this problem efficiently by mimicking gradient based method, namely by iteratively removing one or multiple links at a time from the network. Specifically, using matrix perturbation analysis we formulate an optimization problem involving the eigenvector associated with the largest eigenvalue of the adjacency matrix and whose solution is equal to a suboptimal solution to the original problem. This strategy also enables us to overcome the combinatorial issue of the problem. Distributed algorithms to estimate the eigenvector and to verify network's connectivity are then proposed which facilitate us to solve the new optimization problem. In addition, topological insights into the proposed algorithm and optimality of its solution are also discussed. Finally, the proposed distributed design method is demonstrated and evaluated via several numerical examples.},

  doi          = {10.1109/TNSE.2018.2813426},

  journal      = {IEEE Transactions on Network Science and Engineering},

  number       = 3,

  volume       = 6,

  place        = {United States},

  year         = {Mon Mar 12 00:00:00 EDT 2018},

  month        = {Mon Mar 12 00:00:00 EDT 2018}

}

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