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UID:42@lincs.fr
DTSTART;TZID=Europe/Paris:20161005T140000
DTEND;TZID=Europe/Paris:20161005T150000
DTSTAMP:20170313T170813Z
URL:https://www.lincs.fr/events/generalized-threshold-based-epidemics-in-r
 andom-graphs-the-power-of-extreme-values/
SUMMARY:Generalized Threshold-Based Epidemics in Random Graphs: the Power
 of Extreme Values
DESCRIPTION:Bootstrap percolation is a well-known activation process in a
 graph\,in which a node becomes active when it has at least $r$ active
 neighbors.Such process\, originally studied on regular structures\, has
 been recentlyinvestigated also in the context of random graphs\, where it
 can serve as a simplemodel for a wide variety of cascades\, such as
 thespreading of ideas\, trends\, viral contents\, etc. over large social
 networks.In particular\, it has been shown that in $G(n\,p)$ the final
 active setcan exhibit a phase transition for a sub-linear number of
 seeds.In this paper\, we propose a unique framework to study
 similarsub-linear phase transitions for a much broader class of graph
 modelsand epidemic processes. Specifically\, we consider i) a generalized
 versionof bootstrap percolation in $G(n\,p)$ with random activation
 thresholdsand random node-to-node influences\; ii) different random graph
 models\,including graphs with given degree sequence and graphs
 withcommunity structure (block model). The common thread of our work is
 toshow the surprising sensitivity of the critical seed set sizeto extreme
 values of distributions\, which makes some systems dramaticallyvulnerable
 to large-scale outbreaks. We validate our results running simulation onboth
 synthetic and real graphs.\n\nJoint work with M. Garetto and G. Torrisi\,
 appeared at ACM SIGMETRIC 2016.
CATEGORIES:Seminars,Youtube
LOCATION:LINCS Meeting Room 40\, 23\, avenue d'Italie\, Paris\, 75013\,
 France
GEO:48.8283983;2.3568972000000485
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TZID:Europe/Paris
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DTSTART:20160327T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
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