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This study explores the integration of Python programming and generative artificial intelligence (AI) to construct and visualize notable graphs in graph theory. This approach bridges theoretical concepts with practical implementations by leveraging Python’s computational capabilities and AI’s pattern-generation techniques. The paper highlights historical examples such as the Königsberg Graph and extends to contemporary applications, demonstrating how AI-driven visualization enhances mathematical learning and research. Integrating Python’s libraries, such as Matplotlib, with generative AI enables automated, interactive, and efficient graph representations. This interdisciplinary approach enhances accessibility to graph theory, making it more engaging for students, educators, and researchers. Additionally, the study discusses the broader implications of combining logic, algorithms, and AI in mathematical problem-solving. By applying modern computational tools, this paper showcases how technological advancements facilitate more profound insights into graph structures, reinforcing their significance in various domains, including network analysis, optimization, and decision-making.

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Integrating Python and Generative AI for Graph Theory Visualization and Problem-Solving

  • 会议论文

  • 发表日期：

    2026年

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  • Shanzhen Gao,
  • Weizheng Gao,
  • Julian Allagan,
  • Jianning Su

摘要

This study explores the integration of Python programming and generative artificial intelligence (AI) to construct and visualize notable graphs in graph theory. This approach bridges theoretical concepts with practical implementations by leveraging Python’s computational capabilities and AI’s pattern-generation techniques. The paper highlights historical examples such as the Königsberg Graph and extends to contemporary applications, demonstrating how AI-driven visualization enhances mathematical learning and research. Integrating Python’s libraries, such as Matplotlib, with generative AI enables automated, interactive, and efficient graph representations. This interdisciplinary approach enhances accessibility to graph theory, making it more engaging for students, educators, and researchers. Additionally, the study discusses the broader implications of combining logic, algorithms, and AI in mathematical problem-solving. By applying modern computational tools, this paper showcases how technological advancements facilitate more profound insights into graph structures, reinforcing their significance in various domains, including network analysis, optimization, and decision-making.