论文标题
在弱连接的网络系统中学习相干群集
Learning Coherent Clusters in Weakly-Connected Network Systems
论文作者
论文摘要
我们为具有紧密连接组件的大规模动态网络提供了一种结构性的模型还原方法。首先，通过在图形拉普拉斯矩阵上建模网络反馈的光谱聚类算法来识别相干组。然后，构建了一个还原的网络，每个节点代表每个相干组的聚集动力学，而降低的网络捕获了组之间的动态耦合。当网络图从权重随机块模型随机生成时，我们在近似误差上提供了上限。最后，数值实验与我们的理论发现保持一致并验证。
We propose a structure-preserving model-reduction methodology for large-scale dynamic networks with tightly-connected components. First, the coherent groups are identified by a spectral clustering algorithm on the graph Laplacian matrix that models the network feedback. Then, a reduced network is built, where each node represents the aggregate dynamics of each coherent group, and the reduced network captures the dynamic coupling between the groups. We provide an upper bound on the approximation error when the network graph is randomly generated from a weight stochastic block model. Finally, numerical experiments align with and validate our theoretical findings.
