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

Journal article

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.

Authors: Clark, GJ

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Taylor and Francis
• Journal: Linear and Multilinear Algebra
• Volume: 72
• Issue: 2
• Pages: 188-202
• Publication date: 2022-12-20
• Acceptance date: 2022-12-04
• DOI: 10.1080/03081087.2022.2158171
• EISSN: 1563-5139
• ISSN: 0308-1087
• Language: English
• Keywords: eigenvector; coefficient of variation; local clustering coefficient; principal ratio; golden ratio