Version 1
: Received: 3 August 2020 / Approved: 5 August 2020 / Online: 5 August 2020 (03:43:11 CEST)
Version 2
: Received: 12 September 2021 / Approved: 13 September 2021 / Online: 13 September 2021 (13:27:39 CEST)
Version 3
: Received: 26 October 2022 / Approved: 26 October 2022 / Online: 26 October 2022 (09:39:11 CEST)
How to cite:
Abdullahi, K.B. Optinalysis: Isometric Isomorphism and Automorphism Through A Looking-Glass. Preprints2020, 2020080072. https://doi.org/10.20944/preprints202008.0072.v1
Abdullahi, K.B. Optinalysis: Isometric Isomorphism and Automorphism Through A Looking-Glass. Preprints 2020, 2020080072. https://doi.org/10.20944/preprints202008.0072.v1
Abdullahi, K.B. Optinalysis: Isometric Isomorphism and Automorphism Through A Looking-Glass. Preprints2020, 2020080072. https://doi.org/10.20944/preprints202008.0072.v1
APA Style
Abdullahi, K.B. (2020). Optinalysis: Isometric Isomorphism and Automorphism Through A Looking-Glass. Preprints. https://doi.org/10.20944/preprints202008.0072.v1
Chicago/Turabian Style
Abdullahi, K.B. 2020 "Optinalysis: Isometric Isomorphism and Automorphism Through A Looking-Glass" Preprints. https://doi.org/10.20944/preprints202008.0072.v1
Abstract
Measures of graph symmetry, similarity, and identity have been extensively studied in graph automorphism and isomorphism detection problems. Nevertheless, graph isomorphism detection remains an open (unsolved) problem for many decades. In this paper, a new and efficient methodological paradigm, called optinalysis, is proposed for symmetry detections, similarity, and identity measures between isometric isomorphs or automorphs. Optinalysis is explained and expressed in clearly stated definitions and prove theorems, which conform to the definitions and theorems of isometry, isomorphism, and automorphism. Analogous to the polynomiality formalization for an efficient algorithm for graph isomorphism detection, optinalysis is however deterministic on polynomial and non-polynomial graph models.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
