Comparing the eigenvector and degree dispersion with the principal ratio of a graph

The principal ratio of a graph is the ratio of the greatest and least entry of its principal eigenvector. Since the principal ratio compares the extreme values of the principal eigenvector it is sensitive to outliers. This can be problematic for graphs (networks) drawn from empirical data. To accoun...

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Main Author: Clark, GJ
Format: Journal article
Language:English
Published: Taylor and Francis 2022
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author Clark, GJ
author_facet Clark, GJ
author_sort Clark, GJ
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description The principal ratio of a graph is the ratio of the greatest and least entry of its principal eigenvector. Since the principal ratio compares the extreme values of the principal eigenvector it is sensitive to outliers. This can be problematic for graphs (networks) drawn from empirical data. To account for this we consider the dispersion of the principal eigenvector (and degree vector). More precisely, we consider the coefficient of variation of the aforementioned vectors, that is, the ratio of the vector's standard deviation and mean. We show how both of these statistics are bounded above by the same function of the principal ratio. Further, this bound is sharp for regular graphs. The goal of this paper is to show that the coefficient of variation of the principal eigenvector (and degree vector) can converge or diverge to the principal ratio in the limit. In doing so, we find an example of a graph family (the complete split graph) whose principal ratio converges to the golden ratio. We conclude with conjectures concerning extremal graphs of the aforementioned statistics.
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spelling oxford-uuid:ce57e3be-9b68-4934-93c4-f1a9ab4775172024-04-16T09:35:15ZComparing the eigenvector and degree dispersion with the principal ratio of a graphJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ce57e3be-9b68-4934-93c4-f1a9ab477517EnglishSymplectic ElementsTaylor and Francis2022Clark, GJThe principal ratio of a graph is the ratio of the greatest and least entry of its principal eigenvector. Since the principal ratio compares the extreme values of the principal eigenvector it is sensitive to outliers. This can be problematic for graphs (networks) drawn from empirical data. To account for this we consider the dispersion of the principal eigenvector (and degree vector). More precisely, we consider the coefficient of variation of the aforementioned vectors, that is, the ratio of the vector's standard deviation and mean. We show how both of these statistics are bounded above by the same function of the principal ratio. Further, this bound is sharp for regular graphs. The goal of this paper is to show that the coefficient of variation of the principal eigenvector (and degree vector) can converge or diverge to the principal ratio in the limit. In doing so, we find an example of a graph family (the complete split graph) whose principal ratio converges to the golden ratio. We conclude with conjectures concerning extremal graphs of the aforementioned statistics.
spellingShingle Clark, GJ
Comparing the eigenvector and degree dispersion with the principal ratio of a graph
title Comparing the eigenvector and degree dispersion with the principal ratio of a graph
title_full Comparing the eigenvector and degree dispersion with the principal ratio of a graph
title_fullStr Comparing the eigenvector and degree dispersion with the principal ratio of a graph
title_full_unstemmed Comparing the eigenvector and degree dispersion with the principal ratio of a graph
title_short Comparing the eigenvector and degree dispersion with the principal ratio of a graph
title_sort comparing the eigenvector and degree dispersion with the principal ratio of a graph
work_keys_str_mv AT clarkgj comparingtheeigenvectoranddegreedispersionwiththeprincipalratioofagraph