The usefulness of topological indices

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

doi:10.1016/j.ins.2022.05.038

The usefulness of topological indices

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

^*Corresponding author for this work

Xi'an Technological University
Swiss Distance University of Applied Science
Nankai University
UMIT-The Health and Life Sciences University
Tampere University
Upper Austria University of Applied Sciences
Shahid Rajaee Teacher Training University
Silo AI

Research output: Contribution to journal › Article › peer-review

30 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. (c) 2022 Published by Elsevier Inc.

Original language	English
Pages (from-to)	143-151
Number of pages	9
Journal	Information Sciences
Volume	606
DOIs	https://doi.org/10.1016/j.ins.2022.05.038
Publication status	Published - Aug 2022
Externally published	Yes

Keywords

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

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10.1016/j.ins.2022.05.038

Cite this

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title = "The usefulness of topological indices",

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. (c) 2022 Published by Elsevier Inc.",

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T1 - The usefulness of topological indices

AU - Ma, Yuede

AU - Dehmer, Matthias

AU - Künzi, Urs Martin

AU - Tripathi, Shailesh

AU - Ghorbani, Modjtaba

AU - Tao, Jin

AU - Emmert-Streib, Frank

PY - 2022/8

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N2 - 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. (c) 2022 Published by Elsevier Inc.

AB - 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. (c) 2022 Published by Elsevier Inc.

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KW - Graphs

KW - Networks

KW - Quantitative graph theory

KW - Topological graph measures

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SN - 0020-0255

VL - 606

SP - 143

EP - 151

JO - Information Sciences

JF - Information Sciences

ER -

The usefulness of topological indices

Abstract

Keywords

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