Item Details

title

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

author

Kotsonis, Rebecca

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

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

type

Thesis