A New Look at Clustering Coefficients with Generalization to Weighted and Multi-Faction Networks

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.

Grantor dc:publisher

Wake Forest University

Year dc:date.issued

2017

Author dc:creator

• Kotsonis, Rebecca

• Clustering coefficient

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10339/82258

OAI identifier oai:identifier

oai:wakespace.lib.wfu.edu:10339/82258