Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Computational Optimization and Applications
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Parallel algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Automatically configuring algorithms for scaling performance
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Parameter learning for latent network diffusion
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
| Hi-index | 0.00 |
We study the problem of maximizing the amount of stochastic diffusion in a network by acquiring nodes within a certain limited budget. We use a Sample Average Approximation (SAA) scheme to translate this stochastic problem into a simulation-based deterministic optimization problem, and present a detailed empirical study of three variants of the problem: where all purchases are made upfront, where the budget is split but one still commits to purchases from the outset, and where one has the ability to observe the stochastic outcome of the first stage in order to "re-plan" for the second stage. We apply this to a Red Cockaded Woodpecker conservation problem. Our results show interesting runtime distributions and objective value patterns, as well as a delicate trade-off between spending all budget upfront vs. saving part of it for later.