Data-driven resource allocation for controlling spreading processes
Cameron Nowzari, George Pappas, Shuo Han

Citation
Cameron Nowzari, George Pappas, Shuo Han. "Data-driven resource allocation for controlling spreading processes". Talk or presentation, 29, October, 2014; Poster presented at the 2014 TerraSwarm Annual Meeting.

Abstract
We propose a novel mathematical framework, based on conic geometric programming, to control viral spreading processes in an unknown contact network. We assume that the spreading process takes place in an unknown weighted, directed network and that we only have access to empirical measurements of the temporal behavior of the spreading process over a finite time interval. In most practical cases, identifying the network structure from dynamic observations is an ill-posed problem, in the sense that there are generally many networks that are consistent with the observed dynamics. In this work we take an approach in line with behavioral systems theory and propose a novel optimization framework in which we exactly characterize the set of all networks that are consistent with the empirical measurements as a cone of feasible networks and solve a conic geometric program to design robust controllers able to contain the spreading process for all networks in the cone.

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Citation formats

• HTML

  Cameron Nowzari, George Pappas, Shuo Han. <a
  href="http://www.terraswarm.org/pubs/392.html"><i>Data-driven
  resource allocation for controlling spreading
  processes</i></a>, Talk or presentation,  29,
  October, 2014;  Poster presented at the <a
  href="http://www.terraswarm.org/conferences/14/annual"
  >2014 TerraSwarm Annual Meeting</a>.

• Plain text

  Cameron Nowzari, George Pappas, Shuo Han. "Data-driven
  resource allocation for controlling spreading
  processes". Talk or presentation,  29, October, 2014; 
  Poster presented at the <a
  href="http://www.terraswarm.org/conferences/14/annual"
  >2014 TerraSwarm Annual Meeting</a>.

• BibTeX

  @presentation{NowzariPappasHan14_DatadrivenResourceAllocationForControllingSpreading,
      author = {Cameron Nowzari and George Pappas and Shuo Han},
      title = {Data-driven resource allocation for controlling
                spreading processes},
      day = {29},
      month = {October},
      year = {2014},
      note = { Poster presented at the <a
                href="http://www.terraswarm.org/conferences/14/annual"
                >2014 TerraSwarm Annual Meeting</a>.},
      abstract = {We propose a novel mathematical framework, based
                on conic geometric programming, to control viral
                spreading processes in an unknown contact network.
                We assume that the spreading process takes place
                in an unknown weighted, directed network and that
                we only have access to empirical measurements of
                the temporal behavior of the spreading process
                over a finite time interval. In most practical
                cases, identifying the network structure from
                dynamic observations is an ill-posed problem, in
                the sense that there are generally many networks
                that are consistent with the observed dynamics. In
                this work we take an approach in line with
                behavioral systems theory and propose a novel
                optimization framework in which we exactly
                characterize the set of all networks that are
                consistent with the empirical measurements as a
                cone of feasible networks and solve a conic
                geometric program to design robust controllers
                able to contain the spreading process for all
                networks in the cone.},
      URL = {http://terraswarm.org/pubs/392.html}
  }
  

Posted by Cameron Nowzari on 20 Oct 2014.
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