# Time-Frequency Filtering Meets Graph Clustering

arXiv: https://arxiv.org/abs/2510.07503 · HTML: https://mlsanity.com/paper/2510.07503/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Community Detection and Stochastic Block Models](https://mlsanity.com/paper/1703.10146/) (2017)
- [Exact Recovery in the Stochastic Block Model](https://mlsanity.com/paper/1405.3267/) (2014)
- [A Survey on Multi-View Clustering](https://mlsanity.com/paper/1712.06246/) (2017)
- [High-Order Synchrosqueezing Transform for Multicomponent Signals Analysis -- With an Application to Gravitational-Wave Signal](https://mlsanity.com/paper/2004.09399/) (2020)
- [Unsupervised classification of the spectrogram zeros](https://mlsanity.com/paper/2210.05459/) (2022)
- [On the zeros of the spectrogram of white noise](https://mlsanity.com/paper/1708.00082/) (2017)
- [Uncertainty quantification of synchrosqueezing transform under complicated nonstationary noise](https://mlsanity.com/paper/2506.00779/) (2025)
- [Time-Frequency Mode Decomposition for Wind Turbine Vibration Monitoring under Variable Speed Operation](https://mlsanity.com/paper/2507.11919/) (2025)

## Abstract (arXiv)

We show that the problem of identifying different signal components from a time-frequency representation can be equivalently phrased as a graph clustering problem: given a graph G=(V,E) one aims to identify `clusters', subgraphs that are strongly connected and have relatively few connections between them. The graph clustering problem is well studied, we show how these ideas can suggest (many) new ways to identify signal components. Numerical experiments illustrate the ideas.