Online social networks: Measurement, analysis, and applications to distributed information systems

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dc.contributor.advisorDruschel, Peter
dc.creatorMislove, Alan E.
dc.date.accessioned2011-07-25T01:38:50Z
dc.date.available2011-07-25T01:38:50Z
dc.date.issued2009
dc.description.abstractRecently, online social networking sites have exploded in popularity. Numerous sites are dedicated to finding and maintaining contacts and to locating and sharing different types of content. Online social networks represent a new kind of information network that differs significantly from existing networks like the Web. For example, in the Web, hyperlinks between content form a graph that is used to organize, navigate, and rank information. The properties of the Web graph have been studied extensively, and have lead to useful algorithms such as PageRank. In contrast, few links exist between content in online social networks and instead, the links exist between content and users, and between users themselves. However, little is known in the research community about the properties of online social network graphs at scale, the factors that shape their structure, or the ways they can be leveraged in information systems. In this thesis, we use novel measurement techniques to study online social networks at scale, and use the resulting insights to design innovative new information systems. First, we examine the structure and growth patterns of online social networks, focusing on how users are connecting to one another. We conduct the first large-scale measurement study of multiple online social networks at scale, capturing information about over 50 million users and 400 million links. Our analysis identifies a common structure across multiple networks, characterizes the underlying processes that are shaping the network structure, and exposes the rich community structure. Second, we leverage our understanding of the properties of online social networks to design new information systems. Specifically, we build two distinct applications that leverage different properties of online social networks. We present and evaluate Ostra, a novel system for preventing unwanted communication that leverages the difficulty in establishing and maintaining relationships in social networks. We also present, deploy, and evaluate PeerSpective, a system for enhancing Web search using the natural community, structure in social networks. Each of these systems has been evaluated on data from real online social networks or in a deployment with real users.
dc.format.mimetypeapplication/pdf
dc.identifier.callnoTHESIS COMP. SCI. 2009 MISLOVE
dc.identifier.citationMislove, Alan E.. "Online social networks: Measurement, analysis, and applications to distributed information systems." (2009) Diss., Rice University. <a href="https://hdl.handle.net/1911/61861">https://hdl.handle.net/1911/61861</a>.
dc.identifier.urihttps://hdl.handle.net/1911/61861
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectComputer science
dc.subjectApplied sciences
dc.titleOnline social networks: Measurement, analysis, and applications to distributed information systems
dc.typeThesis
dc.type.materialText
thesis.degree.departmentComputer Science
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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