End-to-End Influence Maximization in the Field


Bryan Wilder, Laura Onasch-Vera, Juliana Hudson, Jose Luna, Nicole Wilson, Robin Petering, Darlene Woo, Milind Tambe, and Eric Rice. 2018. “End-to-End Influence Maximization in the Field.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).


This work is aims to overcome the challenges in deploying influence maximization to support community driven interventions. Influence maximization is a crucial technique used in preventative health interventions, such as HIV prevention amongst homeless youth. Drop-in centers for homeless youth train a subset of youth as peer leaders who will disseminate information about HIV through their social networks. The challenge is to find a small set of peer leaders who will have the greatest possible influence. While many algorithms have been proposed for influence maximization, none can be feasibly deployed by a service provider: existing algorithms require costly surveys of the entire social network of the youth to provide input data, and high performance computing resources to run the algorithm itself. Both requirements are crucial bottlenecks to widespread use of influence maximization in real world interventions. To address the above challenges, this innovative applications paper introduces the CHANGE agent for influence maximization. CHANGE handles the end-to-end process of influence maximization, from data collection to peer leader selection. Crucially, CHANGE only surveys a fraction of the youth to gather network data and minimizes computational cost while providing comparable performance to previously proposed algorithms. We carried out a pilot study of CHANGE in collaboration with a drop-in center serving homeless youth in a major U.S. city. CHANGE surveyed only 18% of the youth to construct its social network. However, the peer leaders it selected reached just as many youth as previously field-tested algorithms which surveyed the entire network. This is the first real-world study of a network sampling algorithm for influence maximization. Simulation results on real-world networks also support our claims.
Last updated on 07/23/2021