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" |