Centrality measures on networks and empirical analysis on activity driven network models

Official URL: http://risc01.sabanciuniv.edu/record=b1615009 (Table of Contents)

Social network analysis involves structural studies on social networks, and it benefits measures of graph theory. Centrality indicators are one of these measures, and they map the characteristics of networks. In the first part of this thesis we study the effects of centrality measures on various network types, including degree, closeness and betweenness centrality. Connections in social networks are rapidly changing, through triadic closures, membership closures or foci closures. Correspondingly, the second part of this thesis and the main objective is first to imitate modeling of an evolving network which thoroughly repeats the behavior of real life networks, and then study triadic closures and topological features of this model. Generation of this model includes power law distribution; thus, its degree distribution and other properties resemble features of three studied datasets. Our analysis on this model gives novel results since the model forms triadic closures as in actual social networks. At the end of our analysis we discuss the reasons of triadic closures in this model with the help of centrality indicators and clustering coefficient.