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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Morley, Patrick R"],"institution":null,"degree_name":"Master of Science in Mathematics (M.S.)","degree_level":"Thesis (restricted to Georgia Southern)","degree_discipline":"Department of Mathematical Sciences","degree_department":null,"school":null,"contributors":["Zhuojun Magnant","Hua Wang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-01-01T08:00:00Z","date_published":"2015-01-01T08:00:00Z","updated_at":"2026-07-24T02:28:22Z","subjects":["ETD","random social network","graph clustering","modeling the spread of disease","Other Mathematics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.georgiasouthern.edu/etd/1314","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhuojun Magnant","Hua Wang"]},{"key":"dc:creator","label":"Author","values":["Morley, Patrick R"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2015-06-30T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Mathematical Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis (restricted to Georgia Southern)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Mathematics (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ETD","random social network","graph clustering","modeling the spread of disease","Other Mathematics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.georgiasouthern.edu/etd/1314"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Using Graph Clustering to Analyze the Spread of an Infectious Disease on a Random Large Social Network Graph"]}]}],"canonical_facts":{"dc:contributor":["Zhuojun Magnant","Hua Wang"],"dc:creator":["Morley, Patrick R"],"dc:date.available":["2015-06-30T07:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://digitalcommons.georgiasouthern.edu/etd/1314"],"dc:subject":["ETD","random social network","graph clustering","modeling the spread of disease","Other Mathematics"],"dc:title":["Using Graph Clustering to Analyze the Spread of an Infectious Disease on a Random Large Social Network Graph"],"thesis:degree_discipline":["Department of Mathematical Sciences"],"thesis:degree_level":["Thesis (restricted to Georgia Southern)"],"thesis:degree_name":["Master of Science in Mathematics (M.S.)"]},"updated_at":"2026-07-24T02:28:22Z"}