Document Type
Article
Publication Date
2018
College/Unit
Eberly College of Arts and Sciences
Department/Program/Center
Mathematics
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.
Digital Commons Citation
Berenhaut, Kenneth S.; Barr, Peter S.; Kogel, Alyssa M.; and Melvin, Ryan L., "Cluster-based network proximities for arbitrary nodal subsets" (2018). Faculty & Staff Scholarship. 1358.
https://researchrepository.wvu.edu/faculty_publications/1358
Source Citation
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). https://doi.org/10.1038/s41598-018-32172-0
Included in
Computer Sciences Commons, Mathematics Commons, Physics Commons, Statistics and Probability Commons
Comments
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
© The Author(s) 2018