The S-metric, the Beichl-Cloteaux approximation, and preferential attachment
Jason Cory Brunson

TL;DR
This paper refines the calculation of the S-metric in network analysis using the Tripathi-Vijay test, compares properties of Barabasi-Albert trees and coauthorship graphs, and discusses their degree assortativity trends.
Contribution
It introduces a streamlined approximation method for the S-metric and applies it to analyze different network types and their degree assortativity behaviors over time.
Findings
Degree variance increased over time in coauthorship graphs despite weaker preferential attachment.
Barabasi-Albert trees showed restricted assortativity, unlike coauthorship graphs.
The new approximation method simplifies the analysis of the S-metric in complex networks.
Abstract
The S-metric has grown popular in network studies, as a measure of ``scale-freeness'' restricted to the collection G(D) of connected graphs with a common degree sequence D=(d_1,\ldots,d_n). The calculation of S depends on the maximum possible degree assortativity r among graphs in G(D). The original method involves a heuristic construction of a maximally assortative graph g*. The approximation by Beichl and Cloteaux involves constructing a possibly disconnected graph g' with r(g') >= r(g*) and requires O(n^2) tests for the graphicality of a degree sequence. The present paper uses the Tripathi-Vijay test to streamline this approximation, and thereby to investigate two collections of graphs: Barabasi-Albert trees and coauthorship graphs of mathematical sciences researchers. Long-term trends in the coauthorship graphs are discussed, and contextualized by insights derived from the BA trees.…
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Taxonomy
TopicsComplex Network Analysis Techniques · Topological and Geometric Data Analysis · Graph theory and applications
