论文标题
弱连接连贯网络系统的光谱聚类和模型降低
Spectral clustering and model reduction for weakly-connected coherent 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出了一种新型的模型还原方法。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。我们的方法理论上是合理的，在随机图设置下。最后，数值实验与我们的理论发现保持一致并验证。
We propose a novel 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. Our approach is theoretically justified under a random graph setting. Finally, numerical experiments align with and validate our theoretical findings.
