BEGIN:VCALENDAR VERSION:2.0 PRODID:-//talks.cam.ac.uk//v3//EN BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:19700329T010000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:19701025T020000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:Isaac Newton Institute Seminar Series SUMMARY:Recognizing graphs formed by spatial random proces ses - Jeanette Janssen (Dalhousie University) DTSTART;TZID=Europe/London:20161212T160000 DTEND;TZID=Europe/London:20161212T164500 UID:TALK69454AThttp://talks.cam.ac.uk URL:http://talks.cam.ac.uk/talk/index/69454 DESCRIPTION:In many real life applications\, network formation can be modelled using a spatial random graph mode l: vertices are embedded in a metric space S\, and pairs of vertices are more likely to be connected if they are closer together in the space. A gener al geometric graph model that captures this concep t is G(n\,w)\, where w is a \; symmetric "link probability" function from SxS to [0\,1]. To guar antee the spatial nature of the random graph\, we requite that this \;function has \;the pro perty that\, for fixed x in S\, w(x\,y) decreases as y is moved further away from x. The function w can be seen as the graph limit of the sequence G(n \,w) as n goes to infinity.
\;We con sider the question: given a large graph or sequenc e of graphs\, how can we determine if they are lik ely the results of such a general geometric random graph process? Focusing on the one-dimensional (l inear) case where S=[0\,1]\, we define a graph par ameter \\Gamma and use the theory of graph limits to show that this parameter indeed measures the&nb sp\;compatibility of the graph with a linear model . \;
This is joint work with Huda Chuangpis hit\, Mahya Ghandehari\, Nauzer Kalyaniwalla\, and Israel Rocha

LOCATION:Seminar Room 1\, Newton Institute CONTACT:INI IT END:VEVENT END:VCALENDAR