Date of Award

Spring 6-9-2023

Document Type

Thesis (Undergraduate)

Department

Quantitative Social Science

First Advisor

Peter DeWan

Abstract

The advent of online social networks has completely transformed the way we communicate, with news, opinions, and ideas now spreading faster than ever before (Guille et al., 2013; Lee et al., 2022). That online social networks have a profound impact on the spread of information suggests further investigation of the relationship between network structure and information diffusion (Light & Moody, 2020). This honors thesis investigates degree assortativity – a measure of large-scale network structure that has often only been a footnote in relevant literature on infor- mation diffusion in online social networks – and its effect on the speed of informa- tion diffusion in online social networks. Two rewiring algorithms (Xulvi-Brunet & Sokolov, 2005) were applied to rewire a Facebook friend circle (n = 44) with varying degree assortativity, ranging from approximately −0.7 to 0.4. For each of the 160 rewired graphs, a random node was selected to infect (i.e., spread information to) its neighbors with probabilities ranging from 10 to 50 percent, and the number of infected nodes after each round of diffusion was recorded. Results suggest that degree assortativity and the speed of information dif- fusion have a strong inverse relationship – disassortative networks spread the same information faster. Moreover, degree assortativity appears to drive the speed of in- formation diffusion more than its correlates, clustering coefficient and average path length (Xulvi-Brunet & Sokolov, 2005).

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