# Spectral clustering and model reduction for weakly-connected coherent network systems

arXiv: https://arxiv.org/abs/2209.13701 · HTML: https://mlsanity.com/paper/2209.13701/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2022.

## Built on by (most cited first, 1 of 1)

- [Learning Coherent Clusters in Weakly-Connected Network Systems](https://mlsanity.com/paper/2211.15301/) (2022)

## Abstract (arXiv)

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.