Keywords

Synthetic networks, attributes based network, social networks, multi link generator, complex network generator

Abstract

An application area of increasing importance is creating agent-based simulations to model human societies. One component of developing these simulations is the ability to generate realistic human social networks. Online social networking websites, such as Facebook, Google+, and Twitter, have increased in popularity in the last decade. Despite the increase in online social networking tools and the importance of studying human behavior in these networks, collecting data directly from these networks is not always feasible due to privacy concerns. Previous work in this area has primarily been limited to 1) network generators that aim to duplicate a small subset of the original network's properties and 2) problem-specific generators for applications such as the evaluation of community detection algorithms. In this thesis, we extended two synthetic network generators to enable them to duplicate the properties of a specific dataset. In the first generator, we consider feature similarity and label homophily among individuals when forming links. The second generator is designed to handle multiplex networks that contain different link types. We evaluate the performance of both generators on existing real-world social network datasets, as well as comparing our methods with a related synthetic network generator. In this thesis, we demonstrate that the proposed synthetic network generators are both time efficient and require only limited parameter optimization.

Notes

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Graduation Date

2014

Semester

Fall

Advisor

Sukthankar, Gita

Degree

Master of Science in Computer Engineering (M.S.Cp.E.)

College

College of Engineering and Computer Science

Department

Electrical Engineering and Computer Science

Degree Program

Computer Engineering

Format

application/pdf

Identifier

CFE0005532

URL

http://purl.fcla.edu/fcla/etd/CFE0005532

Language

English

Release Date

December 2014

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic

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