Artificial Intelligence for Low-Resource Communities: Influence Maximization in an Uncertain World
Amulya Yadav

TL;DR
This paper develops influence maximization algorithms tailored for uncertain social networks in low-resource communities and demonstrates their effectiveness through real-world deployment among homeless youth to raise HIV awareness.
Contribution
It introduces new influence maximization algorithms that handle uncertainties in social networks and applies them in real-world settings for social good.
Findings
Algorithms improved influence spread by 160% over existing methods.
Successful deployment among homeless youth demonstrated real-world impact.
Addressed uncertainties in social network influence models effectively.
Abstract
The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict society is enormous, particularly in the areas of healthcare, conservation and public safety and security. Many problems in these domains involve harnessing social networks of under-served communities to enable positive change, e.g., using social networks of homeless youth to raise awareness about Human Immunodeficiency Virus (HIV) and other STDs. Unfortunately, most of these real-world problems are characterized by uncertainties about social network structure and influence models, and previous research in AI fails to sufficiently address these uncertainties. This thesis addresses these shortcomings by advancing the state-of-the-art to a new generation of algorithms for interventions in social networks. In particular, this thesis describes the design and development of new influence maximization…
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