Preprint Article Version 1 This version is not peer-reviewed

Relating Vertex and Global Graph Entropy in Randomly Generated Graphs

Version 1 : Received: 22 May 2018 / Approved: 22 May 2018 / Online: 22 May 2018 (11:59:26 CEST)

A peer-reviewed article of this Preprint also exists.

Tee, P.; Parisis, G.; Berthouze, L.; Wakeman, I. Relating Vertex and Global Graph Entropy in Randomly Generated Graphs. Entropy 2018, 20, 481. Tee, P.; Parisis, G.; Berthouze, L.; Wakeman, I. Relating Vertex and Global Graph Entropy in Randomly Generated Graphs. Entropy 2018, 20, 481.

Journal reference: Entropy 2018, 20, 481
DOI: 10.3390/e20070481

Abstract

Combinatoric measures of entropy capture the complexity of a graph, but rely upon the calculation of its independent sets, or collections of non-adjacent vertices. This decomposition of the vertex set is a known NP-Complete problem and for most real world graphs is an inaccessible calculation. Recent work by Dehmer et al. and Tee et al. identified a number of alternative vertex level measures of entropy that do not suffer from this pathological computational complexity. It can be demonstrated that they are still effective at quantifying graph complexity. It is intriguing to consider whether there is a fundamental link between local and global entropy measures. In this paper, we investigate the existence of correlation between vertex level and global measures of entropy, for a narrow subset of random graphs. We use the greedy algorithm approximation for calculating the chromatic information and therefore Körner entropy. We are able to demonstrate close correlation for this subset of graphs and outline how this may arise theoretically.

Subject Areas

graph entropy; chromatic classes; random graphs

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