Over the last twenty years, as biological, technological, and social net-
works have risen in prominence and importance, the study of complex networks has attracted researchers from a wide range of fields. As a result,
there is a large and diverse body of literature concerning the properties and
development of models for complex networks. However, many of the models
that have been previously developed, although quite successful at capturing
many observed properties of complex networks, have failed to capture the
fundamental semantics of the networks. In this thesis, we propose a robust
and general model for complex networks that incorporates at a fundamental level semantic information. We show that for a large range of average
degrees and with a suitable choice of parameters, this model exhibits the
three hallmark properties of complex networks: small diameter, clustering,
and skewed degree distribution. Additionally, we provide a structural interpretation of assortativity and apply this strucutral assortativity to the
random dot product graph model. We also extend the results of Chung,
Lu, and Vu on the spectral gap of the expected degree sequence model to
a general class of random graph models with independent edges. We apply
this result to the recently developed Stochastic Kronecker graph model of
Leskovec, Chakrabarti, Kleinberg, and Faloutsos.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/26548 |
| Date | 17 November 2008 |
| Creators | Young, Stephen J. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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