Towards Detecting Structural Branching and Cyclicity in Graphs: A Polynomial-based Approach

Matthias Dehmer, Zengqiang Chen, Frank Emmert-Streib, Abbe Mowshowitz, Yongtang Shi, Shailesh Tripathi, Yusen Zhang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Structural properties of graphs and networks have been investigated across scientific disciplines ranging from mathematics to structural chemistry. Structural branching, cyclicity and, more generally, connectedness are well-known examples of such properties. In particular, various graph measures for detecting structural branching and cyclicity have been investigated. These measures are of limited applicability since their interpretation relies heavily on a certain definition of structural branching. In this paper we define a related measure, taking an approach to measurement similar to that of Lovász and Pelikán (On the eigenvalues of trees, Periodica Mathematica Hungarica, Vol. 3 (1–2), 1973, 175–182). We define a complex valued polynomial which also has a unique positive root. Analytical and numerical results demonstrate that this measure can be interpreted as a structural branching and cyclicity measure for graphs. Our results generalize the work of Lovász and Pelikán since the measure we introduce is not restricted to trees.

Original languageEnglish
Pages (from-to)19-28
Number of pages10
JournalInformation Science
Volume471
DOIs
Publication statusPublished - Jan 2019

Keywords

  • Data science
  • Graphs
  • Networks
  • Quantitative graph theory
  • Structural branching

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