Date of Award
Campus Access Thesis
Master of Science (MS)
Complex networks represent an extensive variety of systems in nature and human interactions. Networks are graphs that describe the structures of interacting systems and give substantial information about the patterns of connections between the nodes in a particular system. In turn, knowing about the structure of networks and their arrangements enables one to make certain types of predictions about their behavior. With that larger motivation, this thesis research emphasizes different measurement metrics such as degree distribution, assortativity and clustering coefficients, transitivity, modularity, network diameter, and the average path length to associate the configurations of the different networks to determine certain types of behavior. The main focus of this thesis is on social networks, where the assortative patterns of social networks were identified. The various parameters used in the study of the networks were calculated and defined using the software packages Networkx and Gephi. The different types of networks are from the Stanford Network Analysis Project (SNAP) website. In particular, the focus is on using the numerical values of the coefficients to infer differences in the forms of contact in different social networks. The ability to do so has implications for detecting preferences when it comes to the relations between groups of people in social networks. As a result of social networks displaying assortative behaviors, the data indicates that these networks could also project some traits of `narrow-mindedness' due to the formation of different clusters. Another significant repercussion of this research is the ability of a community to thrive successfully based on the interactions of the people with one another.
Moreau, Michele, "Centrality Measures in Social Networks" (2012). Graduate Masters Theses. 151.