Algorithms for graph theory matching problems based on computer science

Jingjing Jin ()
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Jingjing Jin: College of General Education, Fujian Chuanzheng Communications College, Fujian, Fuzhou 350007, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 08, 1-19

Abstract: Existing graph-matching algorithms only use the graph’s edges to transmit information and cannot perform hierarchical reasoning and fusion. Although they have achieved good results in graph matching, problems exist such as planarization and insufficient representation of graph data. This paper aims to study graph theory matching algorithms based on computer science, solve matching problems on large-scale graphs, reduce computation time, and improve solving efficiency. This paper studied graph theory matching algorithms based on computer science, including heuristic algorithm-based matching algorithms and machine learning-based matching algorithms; the specific matching problem was mathematically analyzed and transformed into a matching problem in graph theory, which included defining the input and output of the problem, determining the structure and properties of nodes and edges in the graph, and clarifying the required matching and constraint conditions. The experimental results of this paper showed that the average optimal matching rates of the Kuhn–Munkres algorithm, Gale–Shapley (GS)-based algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM) algorithm in complex scenarios were 62.05%, 58.42%, 82.79%, 81.99% and 83.50%, respectively. Studying graph theory matching algorithms based on computer science is of great significance. It can not only solve practical application problems and improve computational efficiency, but also promote algorithm optimization, which helps to improve the processing speed and efficiency of practical problems.

Keywords: Computer science; graph theory matching problem; genetic algorithm; Gale–Shapley algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124410055

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