Difference between revisions of "CATS-May-13-2013"
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and is also interested in energy and other domains where her work may | and is also interested in energy and other domains where her work may | ||
apply. | 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 | ||
Revision as of 20:15, 25 April 2013
Title[edit]
Network Diffusion & Node-Weighted Steiner Trees
Speaker[edit]
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[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
