The usefulness of topological indices

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

Tutkimustuotos: ArtikkeliScientificvertaisarvioitu

Abstrakti

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.

AlkuperäiskieliEnglanti
Sivut143-151
Sivumäärä9
JulkaisuInformation Sciences
Vuosikerta606
DOI - pysyväislinkit
TilaJulkaistu - elok. 2022
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Julkaisufoorumi-taso

  • Jufo-taso 2

!!ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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