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

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abstract
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.
subject
Clustering coefficient
geodesic distance
multi-faction networks
Multiple random walks
two-mode networks
weighted networks
contributor
Kotsonis, Rebecca (author)
Berenhaut, Kenneth S (committee chair)
Erhardt, Robert J (committee member)
Hepler, Staci A (committee member)
date
2017-06-15T08:36:22Z (accessioned)
2019-06-14T08:30:12Z (available)
2017 (issued)
degree
Mathematics and Statistics (discipline)
embargo
2019-06-14 (terms)
identifier
http://hdl.handle.net/10339/82258 (uri)
language
en (iso)
publisher
Wake Forest University
title
A New Look at Clustering Coefficients with Generalization to Weighted and Multi-Faction Networks
type
Thesis

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