##基于自适应的图神经网络的复杂性分析

Abstract

This paper presents a novel framework for analyzing the complexity of graph neural networks (GNNs) utilizing adaptive graph neural networks (GNNs). The core aim is to automatically identify and quantify the complexity of a GNN, providing actionable complexity control strategies. We propose a dynamic complexity adjustment mechanism based on the inherent properties of the network's graph structure. The framework leverages adaptive learning algorithms to dynamically adjust model parameters, enabling a more flexible and automated approach to complexity assessment. This research addresses limitations of traditional complexity analysis methods by offering a robust and adaptable solution for GNNs. The paper details the proposed method, its implementation, and demonstrates its effectiveness through illustrative examples. The core mechanism centers around a self-adjusting network architecture that continuously optimizes the model's parameters based on the network's characteristics. The resulting analysis offers a significant advancement in the field of GNN complexity understanding.