Applications of Machine Learning in Graph Theory Optimization
Abstract
Graph theory optimization has long been integral to solving complex problems in computer science, logistics, and network design. However, traditional algorithms often falter when faced with dynamic, large-scale, or noisy datasets. Machine learning (ML) has emerged as a transformative tool, augmenting classical graph methods with adaptive, data-driven solutions. This paper examines key ML techniques—including graph neural networks (GNNs), reinforcement learning (RL), and differentiable optimization—and their applications in optimizing graph-based systems.
How to Cite This Article
Pierre-Simon Laplace (2025). Applications of Machine Learning in Graph Theory Optimization . International Journal of Applied Mathematics and Numerical Research (IJAMNR), 1(1), 09-10.