Version 1
: Received: 17 July 2023 / Approved: 19 July 2023 / Online: 20 July 2023 (08:26:05 CEST)
How to cite:
Chen, C.; Huang, J. Multi-Objectives Nonlinear Interval PageRank Algorithm Based on the Constrained Interval Arithmetic. Preprints2023, 2023071392. https://doi.org/10.20944/preprints202307.1392.v1
Chen, C.; Huang, J. Multi-Objectives Nonlinear Interval PageRank Algorithm Based on the Constrained Interval Arithmetic. Preprints 2023, 2023071392. https://doi.org/10.20944/preprints202307.1392.v1
Chen, C.; Huang, J. Multi-Objectives Nonlinear Interval PageRank Algorithm Based on the Constrained Interval Arithmetic. Preprints2023, 2023071392. https://doi.org/10.20944/preprints202307.1392.v1
APA Style
Chen, C., & Huang, J. (2023). Multi-Objectives Nonlinear Interval PageRank Algorithm Based on the Constrained Interval Arithmetic. Preprints. https://doi.org/10.20944/preprints202307.1392.v1
Chicago/Turabian Style
Chen, C. and Jih-Jeng Huang. 2023 "Multi-Objectives Nonlinear Interval PageRank Algorithm Based on the Constrained Interval Arithmetic" Preprints. https://doi.org/10.20944/preprints202307.1392.v1
Abstract
This paper presents a novel algorithm, the multi-objective nonlinear interval PageRank (MONIPR) Algorithm. This algorithm extends the conventional PageRank (PR) algorithm, integrating nonlinear interval computation and multi-objective considerations into the computation process. The MONIPR algorithm bridges a research gap in integrating nonlinear interval approaches and multi-objective problems into the PR algorithm, which were traditionally treated separately. In addition, this research adopts the constrained interval arithmetic proposed to address the limitations of the conventional PR algorithm, which tends to overlook uncertainties and results in deterministic outcomes. In addition, we use a numerical example to demonstrate the proposed algorithm, contrasting it with the conventional approaches. A numerical example demonstrates the algorithm's performance by comparing the rank intervals obtained with the traditional crisp Markov chain and PR algorithm. The results highlight the ability of the proposed algorithm to provide a range of possible rank intervals, considering both uncertainty and multi-objectives. The findings suggest potential applications in decision-making, uncertainty quantification, and systems analysis.
Computer Science and Mathematics, Applied Mathematics
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.
