The Influence of Social Network Graph Structure on Disease Dynamics in a Simulated Environment Page: 34
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FIGURE 2.16. Betweenness centrality illustrated by Node 6
TABLE 2.6. Relative betweenness centrality measures for vertices in Figure 2.16
greater generalization is considered in determining information centrality. It is possible that a non-
geodesic path is of greater significance than all other paths. Information centrality takes this point
under consideration. All paths, both geodesic and non-geodesic, are weighted when information
centrality is calculated. Information centrality was not implemented in this research.
Building computational models for simulating disease spread is challenging, at best. The un-
derlying framework of an outbreak model must emulate complex behaviors without becoming too
computationally expensive. A graph theoretical approach allows this social environment to be rep-
resented in a simple format, i.e. nodes and edges. The nodes of a graph represent the individuals in
a population and the edges in the graph correspond to relationships among individuals. This basic
construct creates a foundation for disease simulation.
The SIR infectious disease model is very compatible with a graph-based social network. This
research explores two methods for implementing a simulated disease outbreak. The first technique,
implemented in Chapter 3, generates the social network simultaneously as the disease proliferates.
v C (v) v CA(v)
1 0.01 7 0.07
2 0.06 8 0.36
3 0.05 9 0.05
4 0.06 10 0.00
5 0.01 11 0.02
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Johnson, Tina V. The Influence of Social Network Graph Structure on Disease Dynamics in a Simulated Environment, dissertation, December 2010; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc33173/m1/44/: accessed May 23, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; .