Assortativity analysis of complex real-world networks using the principal components of the centrality metrics

Natarajan Meghanathan

International Journal of Data Science, 2024, vol. 9, issue 1, 79-97

Abstract: Principal component analysis (PCA) captures the variations in the data spread over m features into n dominating principal components (typically, n < m, if the features are correlated) that are orthogonal to each other. Assortativity analysis for complex networks has been so far conducted just with the degree centrality metric or some node-level metric of interest, but not with respect to more than one metric. In this paper, we show that PCA can be used to determine the assortativity index (AI) of complex real-world networks with respect to the four major centrality metrics (degree, eigenvector, betweenness, and closeness) considered together (as features representing the data) for the nodes. Assortativity index of the network is computed as the weighted average (AI_PC^w.avg) of the assortativity indices of the network with respect to each principal component, and the weights are the eigenvalues of the eigenvectors corresponding to the principal components.

Keywords: PCA; principal component analysis; assortativity index; centrality metrics; degree centrality; real-world networks. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=135945 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdsci:v:9:y:2024:i:1:p:79-97

Access Statistics for this article

More articles in International Journal of Data Science from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().