Using Graph Clustering to Analyze the Spread of an Infectious Disease on a Random Large Social Network Graph

<p>The purpose of this work is to analyze the spread of an infectious disease on a random large social network graph. The goal is to determine if graph clustering techniques are a viable option to reduce workload of analyzing of a large data set. A random graph generator was developed using characteristics from the Forest Fire Model. We then use this graph to model the spread of an infectious disease. We develop a preliminary trivial reduction method in which to use as a baseline to formulate and compare more efficient reduction methods. The use of basic statistics ensures the reductions mirror the spread of the disease on our initial random large social network graph.</p>

Name thesis:degree_name

Master of Science in Mathematics (M.S.)

Level thesis:degree_level

Thesis (restricted to Georgia Southern)

Discipline thesis:degree_discipline

Department of Mathematical Sciences

Year dc:date.available

2015

Author dc:creator

• Morley, Patrick R

Contributors dc:contributor

• Zhuojun Magnant
• Hua Wang

• ETD
• random social network
• graph clustering
• modeling the spread of disease
• Other Mathematics

Repository record dc:identifier

https://digitalcommons.georgiasouthern.edu/etd/1314

OAI identifier oai:identifier

oai:digitalcommons.georgiasouthern.edu:etd-2369