Dissertation/Thesis Abstract

Maximizing Influence of Simple and Complex Contagion on Real-World Networks
by Moores, Geoffrey, M.S., University of Maryland, College Park, 2020, 109; 27955400
Abstract (Summary)

Contagion spread over networks is used to model many important real-world processes from a wide variety of domains including epidemiology, marketing, and systems engineering. A large body of research provides strong theoretical guarantees on simple contagion models, but recent research identifies many real-world processes that feature complex contagions whose spread may depend on multiple exposures or other complex criteria.

We present a rigorous study of real-world and artificial networks across simple and complex contagion models. We identify domain-dependent features of real-world networks extracted from publicly-available networks as a guide to solving contagion-related decision problems. We then examine the performance of multiple influence-maximization algorithms across a space of networks and contagion models to develop an experimentally justified guide of best practices for related problems. In particular, genetic algorithms are an extremely viable candidate for these problems, especially with complex graphs and processes.

Indexing (document details)
Advisor: Srinivasan, Aravind
Commitee: Vullikanti, Anil, Dickerson, John
School: University of Maryland, College Park
Department: Computer Science
School Location: United States -- Maryland
Source: MAI 82/3(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Computer science, Information Technology, Applied Mathematics
Keywords: Community Detection, Complex Contagion, Genetic Algorithms, Influence Maximization, Real-World Networks
Publication Number: 27955400
ISBN: 9798678113108
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