Entry Chakrabarti:2008:ETR from tissec.bib

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BibTeX entry

@Article{Chakrabarti:2008:ETR,
  author =       "Deepayan Chakrabarti and Yang Wang and Chenxi Wang and
                 Jurij Leskovec and Christos Faloutsos",
  title =        "Epidemic thresholds in real networks",
  journal =      j-TISSEC,
  volume =       "10",
  number =       "4",
  pages =        "1:1--1:??",
  month =        jan,
  year =         "2008",
  CODEN =        "ATISBQ",
  DOI =          "https://doi.org/10.1145/1284680.1284681",
  ISSN =         "1094-9224 (print), 1557-7406 (electronic)",
  ISSN-L =       "1094-9224",
  bibdate =      "Thu Jun 12 17:52:24 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/tissec.bib",
  abstract =     "How will a virus propagate in a real network? How long
                 does it take to disinfect a network given particular
                 values of infection rate and virus death rate? What is
                 the single best node to immunize? Answering these
                 questions is essential for devising network-wide
                 strategies to counter viruses. In addition, viral
                 propagation is very similar in principle to the spread
                 of rumors, information, and ``fads,'' implying that the
                 solutions for viral propagation would also offer
                 insights into these other problem settings. We answer
                 these questions by developing a nonlinear dynamical
                 system ( NLDS ) that accurately models viral
                 propagation in any arbitrary network, including real
                 and synthesized network graphs. We propose a general
                 epidemic threshold condition for the NLDS system: we
                 prove that the epidemic threshold for a network is
                 exactly the inverse of the largest eigenvalue of its
                 adjacency matrix. Finally, we show that below the
                 epidemic threshold, infections die out at an
                 exponential rate. Our epidemic threshold model subsumes
                 many known thresholds for special-case graphs (e.g.,
                 Erd{\H{o}}s--R{\'e}nyi, BA powerlaw, homogeneous). We
                 demonstrate the predictive power of our model with
                 extensive experiments on real and synthesized graphs,
                 and show that our threshold condition holds for
                 arbitrary graphs. Finally, we show how to utilize our
                 threshold condition for practical uses: It can dictate
                 which nodes to immunize; it can assess the effects of a
                 throttling policy; it can help us design network
                 topologies so that they are more resistant to
                 viruses.",
  acknowledgement = ack-nhfb,
  articleno =    "1",
  fjournal =     "ACM Transactions on Information and System Security",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J789",
  keywords =     "eigenvalue; epidemic threshold; viral propagation",
}

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