2026-09-20T03:14:48Zhttps://wakespace.lib.wfu.edu/server/oai/request

oai:null:10339/822582026-09-16T09:38:30Zcom_10339_14934col_10339_38132

Kotsonis, Rebecca 2017-06-15T08:36:22Z 2019-06-14T08:30:12Z 2017 https://wakespace.lib.wfu.edu/handle/10339/82258 In this thesis, we propose a new method for studying local and global clustering in networks employing random walk pairs. The method is intuitive and directly generalizes standard local and global clustering coefficients to weighted networks and networks containing nodes of multiple types. In the case of two-mode networks, the values obtained for commonly considered social networks are in sharp contrast to those obtained by previous methods, and provide a different viewpoint for clustering. The approach is also applicable in questions related to the general study of segregation and homophily. Applications to existent data sets are considered. en Wake Forest University Clustering coefficient geodesic distance multi-faction networks Multiple random walks two-mode networks weighted networks A New Look at Clustering Coefficients with Generalization to Weighted and Multi-Faction Networks Thesis Berenhaut, Kenneth S Erhardt, Robert J Hepler, Staci A Mathematics and Statistics 2019-06-14 open.access