CATS-May-13-2013

Title

Network Diffusion & Node-Weighted Steiner Trees

Speaker

Evdokia Nikolova is an Assistant Professor at the Computer Science & 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

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

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