Abstract
In this thesis we relax the locally tree-like assumption of configuration modelrandom networks to examine the properties of clustering, and the effects
thereof, on bond percolation. We introduce an algorithmic enumeration
method to evaluate the probability that a vertex remains unattached to the giant
connected component during percolation. The properties of the non-giant,
finite components of clustered networks are also examined, along with the
degree correlations between subgraphs. In a second avenue of research, we
investigate the role of clustering on 2-strain epidemic processes under various
disease interaction schedules. We then examine an 𝑁-generation epidemic by
performing repeated percolation events.
Date of Award | 15 Jun 2022 |
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Original language | English |
Awarding Institution |
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Supervisor | Simon Andrew Dobson (Supervisor) |
Access Status
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