The usefulness of topological indices

Yuede Ma, Matthias Dehmer, Urs Martin Künzi, Shailesh Tripathi, Modjtaba Ghorbani, Jin Tao, Frank Emmert-Streib

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

A huge number of topological graph measures have been defined and investigated. It turned out that various graph measures failed to solve problems meaningfully in the context of characterizing graphs. Reasons for this range from selecting redundant and unfavorable graph invariants and the fact that many of those measures have been defined in an unreflected manner. In this paper, we extend the debate in the literature to find useful properties of structural graph measures. For this, we investigate the usefulness of topological indices for graphs quantitatively by assigning a feature vector to graph that contains ‘useful’ properties represented by certain measures. We show examples and compare the usefulness by using this apparatus based on distance measures and on a agglomerative clustering task.

Original languageEnglish
Pages (from-to)143-151
Number of pages9
JournalInformation Sciences
Volume606
DOIs
Publication statusPublished - Aug 2022

Keywords

  • Data science
  • Graphs
  • Networks
  • Quantitative graph theory
  • Topological graph measures
  • Topological indices

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