Difference between revisions of "CATS-May-13-2013"

From Theory
 
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== Speaker ==
 
== Speaker ==
Evdokia Nikolova is an Assistant Professor at the Computer Science &
+
Jochen Koenemann, Associate Professor, University of Waterloo
Engineering Department at Texas A&M University. Previously she was a
 
postdoctoral associate in the Computer Science and Artificial
 
Intelligence Laboratory at MIT. She graduated with a BA in Applied
 
Mathematics with Economics from Harvard University, MS in Mathematics
 
from Cambridge University (U.K.) and Ph.D. in Computer Science from
 
MIT. She is interested in risk analysis from an algorithmic
 
perspective arising in stochastic optimization, networks, economics
 
and complex systems.  She has worked on applications to transportation
 
and is also interested in energy and other domains where her work may
 
apply.
 
 
 
  
 
== Abstract ==
 
== Abstract ==

Latest revision as of 20:18, 25 April 2013

Title[edit]

Network Diffusion & Node-Weighted Steiner Trees

Speaker[edit]

Jochen Koenemann, Associate Professor, University of Waterloo

Abstract[edit]

The first part of this talk focuses on a network diffusion model that was recently introduced by Goldberg & Liu (SODA'13). Goldberg & Liu's model adapts the earlier linear threshold model of Kempe, Kleinberg & Tardos (KDD'03) in an effort to capture aspects of technology adaptation processes in networks. We present new, improved, yet simple algorithms for the so called Influence Maximization problem in this setting.

A key component of our algorithm is a Langrangean multiplier preserving (LMP) algorithm for the Prize-collecting Node-weighted Steiner Tree problem (PC-NWST). This problem had been studied in prior work by Moss and Rabani (STOC'01 & SICOMP'07) who presented a primal-dual O(log |V|) approximate and LMP algorithm, and showed that this is best possible unless NP=P.

We demonstrate that Moss & Rabani's algorithm for PC-NWST is seriously flawed. We then present a new, fundamentally different primal-dual method achieving the same performance guarantee. Our algorithm introduces several novel features to the primal-dual method that may be of independent interest.

Joint work with Laura Sanita and Sina Sadeghian