Many real-world systems can be represented as networks driven by discrete {em events}, each event identified by the time at which it occurs and the parties involved. An event could be a meeting, a stock trade, a phone call, an email, a gang fight, an online or off-line purchase, a blog post, a conference, or the transmission of an IP packet. Innovations in technology have increased our ability to collect massive amounts of digital data from such networks, which presents both new opportunities and new challenges. In this work, we develop new theoretical models and efficient algorithms that leverage the temporal and relational information inherent in the data to better understand and analyze real-world networks. In particular, we consider three problems: (1) detecting correlated events in communication networks; (2) discovering functional communities; and (3) modeling collaboration in academia. First we present a new stochastic model for event-driven networks, and with it develop two algorithms -- a streaming local algorithm, and an efficient global algorithm -- to detect statistically correlated activity. We demonstrate that our approach, which models each communication channel as its own stochastic process, is better able to accommodate the temporal variability present in real-world communication networks than existing methods. Next we study diffusion processes in information networks, identifying functional communities as groups of individuals who participate in the dissemination of common content by reframing the problem as one of co-clustering sparse matrices. We propose a new co-clustering algorithm that does not require user-specified parameters, and leverages sparsity in the data to run in sublinear time in the size of the matrix. Finally, we build a game-theoretic model for academic collaboration, representing the academic environment as a repeated game in which each researcher tries to maximize his or her academic success. We find analytically that limitations of existing collaboration models may result in misleading predictions about people's behavior.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Information networks--Data processing
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5307
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
x, 106 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Brian Evan Thompson
Subject (authority = ETD-LCSH)
Topic
Computer networks--Monitoring
Subject (authority = ETD-LCSH)
Topic
Computer networks--Mathematical models
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.