Locally Adaptive Optimization: Adaptive Seeding for Monotone Submodular Functions

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. Our main result is a (1−1/e)2 -approximation for the adaptive seeding problem for any monotone submodular function. While adaptive policies are often approximated via non-adaptive policies, our algorithm is based on a novel method we call locally-adaptive policies. These policies combine a non-adaptive global structure, with local adaptive optimizations. This method enables the (1−1/e)2 -approximation for general monotone submodular functions and circumvents some of the impossibilities as...

• Type: Journal
• Year: 2016
• Where: SODA
• Authors: Ashwinkumar Badanidiyuru, Christos H. Papadimitriou, Aviad Rubinstein, Lior Seeman, Yaron Singer
• Keywords: Algorithms, SODA 2016