Bryan Wilder, Amulya Yadav, Nicole Immorlica, Eric Rice, and Milind Tambe. 2017. “Robust, dynamic influence maximization.” In AAMAS International Workshop on Optimization in Multi-Agent Systems (OPTMAS).
This paper focuses on new challenges in influence maximization inspired by non-profits’ use of social networks to effect behavioral
change in their target populations. Influence maximization is a multiagent problem where the challenge is to select the most influential agents
from a population connected by a social network. Specifically, our work is
motivated by the problem of spreading messages about HIV prevention
among homeless youth using their social network. We show how to compute solutions which are provably close to optimal when the parameters
of the influence process are unknown. We then extend our algorithm to
a dynamic setting where information about the network is revealed at
each stage. Simulation experiments using real world networks collected
by the homeless shelter show the advantages of our approach.