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Degree

MS, University of Cincinnati, Engineering and Applied Science: Computer Engineering, 2012.

Abstract

Influence propagation in social networks has been studied extensively in recent years, but in rather ad-hoc or simplistic frameworks. This thesis presents a multi-agent social network model where both the connectivity patterns and the interaction behaviors of agents are taken from a real-world network. Additionally, the agents modeled have plausible cognitive and social characteristics that determine the degree to which they can be influenced by other agents. The agents are classified into different classes based on their relative frequency of interaction with other neighboring agents. Opinions are injected into the system by agents of specific classes and their spread is tracked by propagating tags. The resulting data is used to analyze the influence of agents from each class in the viral spread of ideas under various conditions. The analysis also shows what behavioral factors, at the agent level, have the most significant impact on the spreading of ideas.

Subject Headings

Computer Engineering

Keywords

Agents of Influence; Social Networks; Idea Propagation; Influence; Probabilistic Model; Facebook

Committee / Advisors

Ali Minai, PhD (Committee Chair)
Raj Bhatnagar, PhD (Committee Member)
Karen Davis, PhD (Committee Member)

Pages

79p.

Document number: ucin1342464356
Permalink: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342464356

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