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Social Network Analysis and Mining
Published in:

01-12-2016 | Original Article

Refining the clustering coefficient for analysis of social and neural network data

Authors: Roger Vargas, Frank Garcea, Bradford Z. Mahon, Darren A. Narayan

Published in: Social Network Analysis and Mining | Issue 1/2016

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Abstract

In this paper we show how a deeper analysis of the clustering coefficient in a network can be used to assess functional connections in the human brain. Our metric of edge clustering centrality considers the frequency at which an edge appears across all local subgraphs that are induced by each vertex and its neighbors. This analysis is tied to a problem from structural graph theory in which we seek the largest subgraph that is a Cartesian product of two complete bipartite graphs \(K_{1,m}\) and \(K_{1,1}\). We investigate this property and compare it to other known edge centrality metrics. Finally, we apply the property of clustering centrality to an analysis of functional MRI data obtained, while healthy participants pantomimed object use or identified objects.
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Metadata
Title
Refining the clustering coefficient for analysis of social and neural network data
Authors
Roger Vargas
Frank Garcea
Bradford Z. Mahon
Darren A. Narayan
Publication date
01-12-2016
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2016
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-016-0361-x

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