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#reinforcement learning Open access

Adaptive Graph Theory Algorithm

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

This paper introduces a novel self-adaptive graph theory algorithm designed to optimize the performance of machine learning algorithms through dynamic graph structure adjustment. Traditional graph theory algorithms often operate on static graphs, failing to adequately leverage the inherent variability in data and task characteristics. This algorithm employs a feedback loop that continuously analyzes and modifies the graph's structure and node connections based on observed data, resulting in enhanced machine learning capabilities. The core mechanism revolves around a reinforcement learning-inspired approach, iteratively refining the network topology to minimize loss functions and maximize model performance. The paper details the algorithm's design, implementation, and preliminary results demonstrating its effectiveness in a specific machine learning benchmark.

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