Statistical analysis of network data is an active field of study, in which researchers inves-
tigate graph-theoretic concepts and various probability models that explain the behaviour
of real networks. This thesis attempts to combine two of these concepts: an exponential
random graph and a centrality index. Exponential random graphs comprise the most useful
class of probability models for network data. These models often require the assumption
of a complex dependence structure, which creates certain difficulties in the estimation of
unknown model parameters. However, in the context of dynamic networks the exponential
random graph model provides the opportunity to incorporate a complex network structure
such as centrality without the usual drawbacks associated with parameter estimation. The
thesis employs this idea by proposing probability models that are equivalent to the logistic
regression models and that can be used to explain behaviour of both static and dynamic
networks.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/6290 |
| Date | 09 1900 |
| Creators | Kulmatitskiy, Nikolay |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
Page generated in 0.011 seconds