用图形神经网络预测复杂网络中的稳定状态行为
Priodyuti Pradhan1, Amit Reza2,3
1Department of Computer Science and Engineering, Indian Institute of Information Technology Raichur, Karnataka 584135, India.
Physical review. E
|December 23, 2025
概括
这项研究使用图形神经网络在复杂系统中准确识别信息传播状态. 开发的模型有效地区分了扩散,弱局部化和强局部化状态,使用真实世界的数据.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 机器学习 机器学习
背景情况:
- 复杂系统中的信息传播可以分为扩散,弱局部化和强局部化状态.
- 了解这些传播动态对于分析系统行为至关重要.
研究的目的:
- 应用图形神经网络 (GNN) 模型来学习和识别网络上的线性动态系统中的信息传播状态.
- 开发一个GNN框架,能够准确地区分不同的本地化状态.
主要方法:
- 开发一个图形卷积和基于注意力的神经网络框架.
- 在网络上运行的线性动态系统上训练GNN模型.
- 使用模拟和真实世界的网络数据评估模型的性能.
- 为了解释性,对框架的前向和后向传播的分析推导.
主要成果:
- 经过训练的GNN模型在区分扩散,局部化弱和局部化强的信息传播状态方面取得了很高的准确性.
- 在对真实世界数据集进行评估时,该模型表现出了强大的性能.
- 分析推导为模型的决策过程提供了洞察力.
结论:
- 图形神经网络是分析复杂系统中信息传播动态的有效工具.
- 开发的GNN框架提供了一种强大且易于解释的状态识别方法.
- 这种方法在涉及网络系统的各个领域都有潜在的应用.
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