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== Speaker ==
 
== Speaker ==
Evdokia Nikolova is an Assistant Professor at the Computer Science &
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Jochen Koenemann, Associate Professor, University of Waterloo
Engineering Department at Texas A&M University. Previously she was a
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postdoctoral associate in the Computer Science and Artificial
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== Abstract ==
Intelligence Laboratory at MIT. She graduated with a BA in Applied
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The first part of this talk focuses on a network diffusion model
Mathematics with Economics from Harvard University, MS in Mathematics
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that was recently introduced by Goldberg & Liu (SODA'13). Goldberg &
from Cambridge University (U.K.) and Ph.D. in Computer Science from
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Liu's model adapts the earlier linear threshold model of Kempe,
MIT. She is interested in risk analysis from an algorithmic
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Kleinberg & Tardos (KDD'03) in an effort to capture aspects of
perspective arising in stochastic optimization, networks, economics
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technology adaptation processes in networks. We present new,
and complex systems. She has worked on applications to transportation
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improved, yet simple algorithms for the so called Influence
and is also interested in energy and other domains where her work may
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Maximization problem in this setting.
apply.
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A key component of our algorithm is a Langrangean multiplier
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preserving (LMP) algorithm for the Prize-collecting Node-weighted
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Steiner Tree problem (PC-NWST). This problem had been studied in
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prior work by Moss and Rabani (STOC'01 & SICOMP'07) who presented a
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primal-dual O(log |V|) approximate and LMP algorithm, and showed
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that this is best possible unless NP=P.
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We demonstrate that Moss & Rabani's algorithm for PC-NWST is
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seriously flawed. We then present a new, fundamentally different
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primal-dual method achieving the same performance guarantee. Our
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algorithm introduces several novel features to the primal-dual
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method that may be of independent interest.
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Joint work with Laura Sanita and Sina Sadeghian