This thesis proposal makes the following technical contributions: (i) we provide a definition of the Dynamic Influence Maximization Under Uncertainty (or DIME) problem, which models the problem faced by homeless shelters accurately; (ii) we propose a novel Partially Observable Markov Decision Process (POMDP) model for solving the DIME problem; (iii) we design two scalable POMDP algorithms (PSINET and HEALER) for solving the DIME problem, since conventional POMDP solvers fail to scale up to sizes of interest; and (iv) we test our algorithms effectiveness in the real world by conducting a pilot study with actual homeless youth in Los Angeles. The success of this pilot (as explained later) shows the promise of using influence maximization for social good on a larger scale.
@article{arxiv.1912.02105,
title = {Influence Maximization for Social Good: Use of Social Networks in Low Resource Communities},
author = {Amulya Yadav},
journal= {arXiv preprint arXiv:1912.02105},
year = {2019}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1602.00165