##基于多尺度图神经网络的复杂系统建模

Abstract

This paper introduces a novel approach to modeling complex systems utilizing multi-scale graph neural networks (MS-GNNs). Complex systems, characterized by interconnected components operating at varying scales, pose significant challenges for traditional modeling techniques. This research proposes a framework that decomposes a complex system into sub-systems defined by their respective scales. Each sub-system is then represented as a graph, and a graph neural network (GNN) is applied to learn patterns and relationships within that scale. A key innovation lies in the multi-scale fusion mechanism, which intelligently integrates information from these GNNs at different scales, ultimately constructing a comprehensive representation of the entire system. The core claim is the design of an MS-GNN capable of capturing the interconnectedness of complex systems across multiple scales, facilitating accurate modeling and prediction. The proposed methodology offers a fundamentally new approach combining the strengths of graph neural networks and multi-scale analysis, paving the way for improved understanding and prediction of complex phenomena.