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Cluster-based network proximities for arbitrary nodal subsets

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abstract
The concept of a cluster or community in a network context has been of considerable interest in a variety of settings in recent years. In this paper, employing random walks and geodesic distance, we introduce a unified measure of cluster-based proximity between nodes, relative to a given subset of interest. The inherent simplicity and informativeness of the approach could make it of value to researchers in a variety of scientific fields. Applicability is demonstrated via application to clustering for a number of existent data sets (including multipartite networks). We view community detection (i.e. when the full set of network nodes is considered) as simply the limiting instance of clustering (for arbitrary subsets). This perspective should add to the dialogue on what constitutes a cluster or community within a network. In regards to health-relevant attributes in social networks, identification of clusters of individuals with similar attributes can support targeting of collective interventions. The method performs well in comparisons with other approaches, based on comparative measures such as NMI and ARI.
subject
Applied mathematics
Computer science
Statistics
contributor
Berenhaut, K. (author)
Barr, P. (author)
Kogel, A. (author)
Melvin, R. (author)
date
2020-03-09T18:38:53Z (accessioned)
2020-03-09T18:38:53Z (available)
9/25/18 (issued)
identifier
Berenhaut, K. S., Barr, P. S., Kogel, A. M., & Melvin, R. L. (2018). Cluster-based network proximities for arbitrary nodal subsets. Scientific reports, 8(1), 14371. (citation)
https://doi.org/10.1038/s41598-018-32172-0 (doi)
http://hdl.handle.net/10339/96051 (uri)
language
en (iso)
publisher
Cluster-based network proximities for arbitrary nodal subsets.
rights
https://creativecommons.org/licenses/by/4.0/ (uri)
source
Scientific Reports
title
Cluster-based network proximities for arbitrary nodal subsets
type
Article

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