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X-WR-CALNAME:Eurandom
X-ORIGINAL-URL:https://www.eurandom.tue.nl
X-WR-CALDESC:Events for Eurandom
BEGIN:VTIMEZONE
TZID:UTC
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TZOFFSETFROM:+0000
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DTSTART:20190101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20190117T150000
DTEND;TZID=UTC:20190117T160000
DTSTAMP:20220128T024025
CREATED:20190114T082557Z
LAST-MODIFIED:20190925T094139Z
UID:2440-1547737200-1547740800@www.eurandom.tue.nl
SUMMARY:Eindhoven Stochastic Colloquium
DESCRIPTION:Jeannette Janssen (Dalhousie University) \nRecognizing graphs formed by a spatial random process \nIt is a safe assumption about social networks that links are more often formed between people that have a lot in common. This can be modelled with a simple spatial model for link formation. Individuals are represented by vertices placed in a virtual space that represents their interests and characteristics. Links are formed stochastically so that most links are local\, i.e. links are less likely between vertices that are further apart. \nGiven a graph\, but no information about the spatial embedding\, can we measure whether the graph conforms to a spatial model? Focusing on the case where the space is one-dimensional\, we introduce a parameter\, $\Gamma$\, that provide such a measure. We use the theory of graph limits to show that graphs for which $\Gamma$ is small resemble samples from a spatial model. We also show how\, given a sample from a (1D) spatial model\, essential spatial information about the vertices can be retrieved from the sample. This is joint work with Mahya Ghandehari and Aaron Smith. \n
URL:https://www.eurandom.tue.nl/event/eindhoven-stochastic-colloquium-2/
LOCATION:MF 11-12 (4th floor MetaForum Building\, TU/e)
CATEGORIES:STO Seminar
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