用图神经网络预测复杂网络的稳态行为，准确区分信息传播状态。
Predicting Steady-State Behavior in Complex Networks with Graph Neural Networks
- 基于图卷积与注意力机制建模网络动力系统
- 在真实数据上实现高精度稳态状态分类
- 提供可解释性分析，适合网络动力学研究者
在复杂系统中，信息传播可分为弥散或非局域、弱局域和强局域三种状态。本研究探索图神经网络在学习网络上线性动力系统稳态行为中的应用。提出一种结合图卷积与注意力机制的神经网络框架，用于识别线性动力系统的稳态行为。训练后的模型能以高准确率区分不同传播状态。此外，在真实世界数据上评估了模型性能。为理解模型可解释性，还推导了该框架前向与反向传播的解析表达式。
原文摘要 · Abstract (English)
In complex systems, information propagation can be defined as diffused or delocalized, weakly localized, and strongly localized. This study investigates the application of graph neural network models to learn the behavior of a linear dynamical system on networks. A graph convolution and attention-based neural network framework has been developed to identify the steady-state behavior of the linear dynamical system. We reveal that our trained model distinguishes the different states with high accuracy. Furthermore, we have evaluated model performance with real-world data. In addition, to understand the explainability of our model, we provide an analytical derivation for the forward and backward propagation of our framework.
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