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Locally Adaptive Optimization: Adaptive Seeding for Monotone Submodular Functions

2015
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Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms
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The Adaptive Seeding problem is an algorithmic challenge motivated by influence maximization in social networks: One seeks to select among certain accessible nodes in a network, and then select, adaptively, among neighbors of those nodes as they become accessible in order to maximize a global objective function. More generally, adaptive seeding is a stochastic optimization framework where the choices in the first stage affect the realizations in the second stage, over which we aim to optimize.

doi:10.1137/1.9781611974331.ch31
dblp:conf/soda/BadanidiyuruPRS16
fatcat:otvs6pbgtjaqvc2jhoocv4chb4