[Dmbu-l] The Diffusion of Networking Technologies @ Thu May 3 11am - 12pm [NRG Group meeting]
cmav at bu.edu
Wed May 2 16:00:42 EDT 2012
As we discussed in our previous meeting, we will be joining the talk of
prof. Goldberg featured below. It's on MCS 148 between 11am - 12pm.
Details and abstract follow.
*From:* Google Calendar <calendar-notification at google.com>
*Date:* April 28, 2012 10:59:47 EDT
*To:* "Network Reading Group @ BUCS" <nrg-l at cs.bu.edu>
*Subject:* *[WING] [NRG] Reminder: NRG meeting: The Diffusion of Networking
Technologies @ Thu May 3 11am - 12pm (NRG Calendar)*
*Reply-To:* "goncagursun at gmail.com" <goncagursun at gmail.com>
more details »<https://www.google.com/calendar/event?action=VIEW&eid=c2ljMGU5ZXJzbDVoZ2lmZXJydnVjZmlvbmcgNTYwam42bnQ1aGo0b2YzcnNyaWNoZnB0aW9AZw>
NRG meeting: The Diffusion of Networking Technologies
Title: The Diffusion of Networking Technologies
Presenter: Sharon Goldberg
In the rich and growing literature on diffusion and cascade effects in
social networks, it is assumed that a node's actions are influenced only by
its immediate neighbors in the social network. However, there are other
contexts in which this highly-local view of influence is not applicable.
The diffusion of technologies in communication networks is one important
example; here, a node's actions should also be influenced by remote nodes
that it can communicate with using the new technology. We propose a new
model of technology diffusion inspired by the networking literature on this
topic, and consider an algorithmic problem that is well understood in the
context of social networks, but thus far has only heuristic solutions in
the context of communication networks: determining the smallest seedset of
early adopter nodes, that once activated, cause a cascade that eventually
causes all other nodes in the network to activate as well. Our main result
is an approximation algorithm that returns a seedset that is an $O(r\ell
\log|V|)$-factor larger than then the optimal seedset, where $r$ is the
graph diameter and each node's threshold can take on one of at most $\ell$
possible values. Our results highlight the substantial algorithmic
difference between our problem and the work in diffusion on social networks.
Joint work with Zhenming Liu.
Thu May 3 11am – 12pm Eastern Time
MCS-148, 111 Cummington Street, Boston, MA 02215
goncagursun at gmail.com - creator
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