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

^*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Fachzeitschrift › Artikel › Begutachtung

27 Zitate (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.

Originalsprache	Englisch
Seiten (von - bis)	143-151
Seitenumfang	9
Fachzeitschrift	Information Sciences
Jahrgang	606
DOIs	https://doi.org/10.1016/j.ins.2022.05.038
Publikationsstatus	Veröffentlicht - Aug. 2022

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10.1016/j.ins.2022.05.038

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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 {\textquoteleft}useful{\textquoteright} 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.",

keywords = "Data science, Graphs, Networks, Quantitative graph theory, Topological graph measures, Topological indices",

author = "Yuede Ma and Matthias Dehmer and K{\"u}nzi, \{Urs Martin\} and Shailesh Tripathi and Modjtaba Ghorbani and Jin Tao and Frank Emmert-Streib",

note = "Funding Information: Matthias Dehmer thanks the Austrian Science Funds for supporting this work (project P30031). Yuede Ma was partially supported by Natural Science Basic Research Program of Shaanxi (Program No.2021JM-422). FES would like to thank the Academy of Finland for support (Grant 349043). Publisher Copyright: {\textcopyright} 2022",

year = "2022",

month = aug,

doi = "10.1016/j.ins.2022.05.038",

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pages = "143--151",

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AU - Ma, Yuede

AU - Dehmer, Matthias

AU - Künzi, Urs Martin

AU - Tripathi, Shailesh

AU - Ghorbani, Modjtaba

AU - Tao, Jin

AU - Emmert-Streib, Frank

N1 - Funding Information: Matthias Dehmer thanks the Austrian Science Funds for supporting this work (project P30031). Yuede Ma was partially supported by Natural Science Basic Research Program of Shaanxi (Program No.2021JM-422). FES would like to thank the Academy of Finland for support (Grant 349043). Publisher Copyright: © 2022

PY - 2022/8

Y1 - 2022/8

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.

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.

KW - Data science

KW - Graphs

KW - Networks

KW - Quantitative graph theory

KW - Topological graph measures

KW - Topological indices

UR - https://www.scopus.com/pages/publications/85130595193

U2 - 10.1016/j.ins.2022.05.038

DO - 10.1016/j.ins.2022.05.038

M3 - Article

AN - SCOPUS:85130595193

SN - 0020-0255

VL - 606

SP - 143

EP - 151

JO - Information Sciences

JF - Information Sciences

ER -

The usefulness of topological indices

Abstract

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