Data Management and Mining Lab, Computer Science Dept., UBC

What is SNRG?

SNRG is a reading group that holds weekly meetings to discuss interesting papers and problems, generally related to social networks. Feel free to attend meetings and participate in discussions. All ideas about problems, papers, talks, etc. are welcome. Meetings usually start with stating an interesting problem without getting into possible solutions, and then following up possible approaches to previously stated problems. An informal talk by a volunteer follows, and attendants discuss about the paper(s) as the speaker proceeds. Finally, possible applications of and extensions to the approach are discussed.

When, where, and how?

Currently, in the first Winter term of 2014/2015, meetings are held on Friday 2:30-4:00 pm in ICICS 104 .
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SNRG talks

List of previous and upcoming talks:

Date Speaker Title or Paper Name
Sep 19 Yidan Yidan Liu et al., "Recommending User Generated Item Lists", RecSys 2014
Sep 26 Sharan Vincent Yun Lou et al., "Modeling Non-Progressive Phenomena for Influence Propagation", COSN 2014
Oct 3 Glenn Edith Cohen, "Size-Estimation Framework with Applications to Transitive Closure and Reachability", Journal of Computer and System Sciences 1997
Oct 10 James Edith Cohen et al., "Summarizing Data using Bottom-k Sketches", PODC 2007
Oct 17 James Edith Cohen et al., "Sketch-based Influence Maximization and Computation: Scaling up with Guarantees", ICKM 2014
Oct 24 Sharan Mehrdad Farajtabar et al., "Shaping Social Activity by Incentivizing Users", NIPS 2014
Oct 31 Pei "On the Maximum Quasi-Clique Search Problem"
Nov 7 - Cancelled
Nov 14 Wei "Business-Centric Data Mining: Data-Driven Optimizations in Viral Marketing and Recommender Systems"
Nov 21 Sampoorna Arun V. Sathanur et al., "PHYSENSE: Scalable Sociological Interaction Models for Influence Estimation on Online Social Networks", ISI 2013
Nov 28 Yan Ting Deng et al., "On recommendation problems beyond points of interest", Information Systems 2015
Dec 5 Glenn "Recommendation Influence Maximization"