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All Journal Journal of Fundamental Mathematics and Applications (JFMA) Integra: Journal of Integrated Mathematics and Computer Science
Fujita, Takaaki
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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BOUNDED TREE-DEPTH, PATH-DISTANCE-WIDTH, AND LINEAR-WIDTH OF GRAPHS Fujita, Takaaki
Journal of Fundamental Mathematics and Applications (JFMA) Vol 7, No 2 (2024)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v7i2.24214

Abstract

The study of width parameters and related graph parameters is an activearea of research in graph theory. In this brief paper, we explore the upper and lowerbounds of graph parameters, including path-distance-width, tree-distance-width, tree-depth, and linear-width. These bounds are crucial for understanding the complexityand structure of graphs.
BOUNDING LINEAR-WIDTH AND DISTANCE-WIDTH USING FEEDBACK VERTEX SET AND MM-WIDTH FOR GRAPH Fujita, Takaaki
Journal of Fundamental Mathematics and Applications (JFMA) Vol 8, No 1 (2025)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v8i1.24222

Abstract

Studying the upper and lower bounds of graph parameters is crucial for understanding the complexity and tractability of computational problems, optimizing algorithms, and revealing structural properties of various graph classes. In this brief paper, we explore the upper and lower bounds of graph parameters, including path-distance-width, MM-Width, Feedback Vertex Set, and linear-width. These bounds are crucial for understanding the complexity and structure of graphs.
Note for Soft MultiExpert Graph and MultiSoft MultiExpert Graph Fujita, Takaaki
Integra: Journal of Integrated Mathematics and Computer Science Vol. 3 No. 1 (2026): March
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20263146

Abstract

This paper studies multilevel extensions of soft-set-based graph models for uncertainty aware decision support. We first recall soft, multisoft, soft expert, and multisoft multi expert sets, which encode parameterized and expert-dependent approximations of a universe. Building on these notions, we introduce the MultiSoft Graph, a family of induced subgraphs of a given graph indexed by multilevel parameter blocks, and show that it strictly generalizes classical soft graphs while inducing a canonical multisoft set on the vertex set. We then define Soft MultiExpert Graphs and MultiSoft MultiExpert Graphs, providing a unified framework that jointly handles graph topology, multiple parameters, and expert opinions.
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