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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- strategy,
6(1)1,
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17(2)7,
17(2)8,
17(4)15,
17(4)16,
18(1)1,
18(2)6,
18(3)9,
18(4)14
- utilize,
10(1)3,
11(4)19,
12(2)13,
18(3)11,
18(4)12
- value,
10(3)10,
10(4)4,
11(2)5,
11(4)18,
12(1)6,
12(2)10,
13(1)10,
14(1)9,
14(1)14,
15(1)4,
15(2)9,
15(4)17,
16(1)1,
17(3)10,
18(1)1,
18(2)5,
18(3)10
- very,
1(1)3,
2(1)65,
2(3)269,
11(2)5,
11(3)14,
11(4)18,
13(3)22,
13(3)27,
14(1)2,
15(1)2,
15(3)12,
16(1)1,
16(3)11,
16(4)13,
17(3)10,
17(4)15,
17(4)16,
18(2)7,
18(3)11,
18(4)12,
18(4)14
- virus,
12(2)11
- what,
2(2)138,
12(2)9,
15(2)10,
17(1)2,
17(3)11
- will,
1(1)93,
2(2)159,
2(3)269,
11(3)12,
11(4)20,
12(1)3,
12(2)10,
12(2)11
- would,
2(2)159,
12(2)13,
12(4)22,
13(3)26,
15(2)6,
15(2)10,
16(2)8,
17(3)11,
18(3)9