自编码
可扩展性
计算机科学
网络拓扑
图形
生成模型
拓扑(电路)
理论计算机科学
特征(语言学)
生成语法
信息物理系统
人工神经网络
人工智能
分布式计算
数学
计算机网络
语言学
哲学
组合数学
数据库
操作系统
作者
Yigu Liu,Haiwei Xie,Alfan Presekal,Alexandru Ştefanov,Peter Pálenský
出处
期刊:IEEE Transactions on Smart Grid
[Institute of Electrical and Electronics Engineers]
日期:2023-11-01
卷期号:14 (6): 4968-4971
被引量:3
标识
DOI:10.1109/tsg.2023.3304134
摘要
Synthetic networks aim at generating realistic projections of real-world networks while concealing the actual system information. This paper proposes a scalable and effective approach based on graph neural networks (GNN) to generate synthetic topologies of Cyber-Physical power Systems (CPS) with realistic network feature distribution. In order to comprehensively capture the characteristics of real CPS networks, we propose a generative model, namely Graph-CPS, based on graph variational autoencoder and graph recurrent neural networks. The method hides the sensitive topological information while maintaining the similar feature distribution of the real networks. We used multiple power and communication networks to prove and assess the effectiveness of the proposed method with experimental results.
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