Title: Local clustering coefficient-based assortativity analysis of real-world network graphs

Authors: Natarajan Meghanathan

Addresses: Jackson State University, Mailbox 18839, 1400 John R. Lynch Street, Jackson, Mississippi, MS 39217, USA

Abstract: Assortative index (A. Index) of a network graph is a measure of the similarity of the end vertices of the edges with respect to a node-level metric. Networks were classified as assortative, dissortative or neutral depending on the proximity of the A. index values to 1, -1 or 0 respectively. Degree centrality (DegC) has been traditionally the node-level metric used for assortativity analysis in the literature. In this paper, we propose to analyse assortativity of real-world networks using the local clustering coefficient (LCC) metric: a measure of the probability with which any two neighbours of a vertex are connected. Though DegC and LCC are inversely related, we observe 80% of the 50 real-world network graphs analysed to exhibit similar levels of assortativity. We also observe a real-world network graph to be neutral (i.e., assortative or dissortative) with a probability of 0.6 or above with respect to both DegC and LCC.

Keywords: assortativity index; A. Index; local clustering coefficient; LCC; degree centrality; DegC; correlation; real-world network graph.

DOI: 10.1504/IJNS.2017.083577

International Journal of Network Science, 2017 Vol.1 No.3, pp.187 - 208

Received: 22 Apr 2016
Accepted: 11 Jun 2016

Published online: 04 Apr 2017 *

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