Graph Theory: Advanced Algorithms and Applications, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 10 May 2025 | Viewed by 798

Special Issue Editor


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Department of Computer Science and Information Engineering, Chaoyang University of Technology, Wufeng, Taichung 413310, Taiwan
Interests: graph algorithms; linux system; applications for smart phones; security on IoT
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Special Issue Information

Dear Colleagues,

Graph theory has grown into a significant area of mathematical research, with applications in computer network architecture, electronic circuits, interpersonal graphs, social network analysis, and many other areas requiring fast algorithms for various optimization problems. For this Special Issue, we welcome all research papers on the mathematical, computational, and applied aspects of graph theory.

We invite original research contributions to this Special Issue on “Graph Theory: Advanced Algorithms and Applications”, which aims to report and review recent developments concerning graph theory and discrete mathematics, covering the whole range of this field, from theory to applications.

Prof. Dr. Ruo-Wei Hung
Guest Editor

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Keywords

  • graph algorithms and theory
  • discrete applied mathematics
  • theoretical computer science
  • approximation algorithms
  • interconnection networks
  • time complexity analysis
  • big data model and analysis

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Published Papers (1 paper)

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Research

25 pages, 7252 KiB  
Article
An Efficient Target-to-Area Classification Strategy with a PIP-Based KNN Algorithm for Epidemic Management
by Jong-Shin Chen, Ruo-Wei Hung and Cheng-Ying Yang
Mathematics 2025, 13(4), 661; https://doi.org/10.3390/math13040661 - 17 Feb 2025
Viewed by 402
Abstract
During a widespread epidemic, a large portion of the population faces an increased risk of contracting infectious diseases such as COVID-19, monkeypox, and pneumonia. These outbreaks often trigger cascading effects, significantly impacting society and healthcare systems. To contain the spread, the Centers for [...] Read more.
During a widespread epidemic, a large portion of the population faces an increased risk of contracting infectious diseases such as COVID-19, monkeypox, and pneumonia. These outbreaks often trigger cascading effects, significantly impacting society and healthcare systems. To contain the spread, the Centers for Disease Control and Prevention (CDC) must monitor infected individuals (targets) and their geographical locations (areas) as a basis for allocating medical resources. This scenario is a Target-to-Area (TTA) problem. Previous research introduced the Point-In-Polygon (PIP) technique to address multi-target and single-area TTA problems. PIP technology relies on an area’s boundary points to determine whether a target is within that region. However, when dealing with multi-target, multi-area TTA problems, PIP alone may have limitations. The K-Nearest Neighbors (KNN) algorithm presents a promising alternative, but its classification accuracy depends on the availability of sufficient samples, i.e., known targets and their corresponding geographical areas. When sample data are limited, the effectiveness of KNN is constrained, potentially delaying the CDC’s ability to track and manage outbreaks. For this problem, this study proposes an improved approach that integrates PIP and KNN technologies while introducing area boundary points as additional samples. This enhancement aims to improve classification accuracy and mitigate the impact of insufficient sample data on epidemic tracking and management. Full article
(This article belongs to the Special Issue Graph Theory: Advanced Algorithms and Applications, 2nd Edition)
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