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BEGIN:VEVENT
DTSTART:20181207T220000Z
UID:133246@events.unl.edu
DTSTAMP:20180905T190555Z
ORGANIZER;CN=David Pitts:MAILTO:dpitts2@unl.edu
SUMMARY:Bilal Khan\, Department of Sociology\, UNL
DESCRIPTION:For social scientists studying subpopulations whose members exp
erience disproportionate health issues\, size estimates are important info
rmation as they are necessary for creating effective public policy. At the
same time\, the problem of size estimation is exceedingly difficult when
the subpopulations of interest are hard to reach\, as is the case when soc
ial stigma is associated with group membership or when group members requi
re anonymity because of their involvement in illegal activities. \nThe res
olution of such difficulties is among the concerns of social network analy
sis\, which is one of the many applications of graph theory. In this talk\
, I will present a recent result in social network analysis relating to th
e properties of random graphs. Informally stated\, the question considered
is\: Can one estimate the size of a graph merely from the combinatorics a
nd geometry of a small induced subgraph? I will show that\, suitably forma
lized\, the question can be answered positively for a large class of rando
m graphs. \nIn the second half of the talk\, I will present some open prob
lems in social network analysis\, including some results in disease epidem
iology which have been verified experimentally\, but have yet to be proven
formally. I will also describe new social data collection paradigms based
on emerging mHealth technologies being developed at the University of Neb
raska-Lincoln\, and the implications of these advances on the quantificati
on of social networks. This new and imminent data suggest a need for exten
sions to classical graph-theoretic formalisms\, in anticipation of concomi
tant advances in our understanding of mathematical properties of the Human
Sociome.
LOCATION:Avery Hall Room
URL://events.unl.edu/math/2018/12/07/133246/
DTEND:20181207T225000Z
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