# Network Clustering for Latent State and Changepoint Detection

arXiv: https://arxiv.org/abs/2111.01273 · HTML: https://mlsanity.com/paper/2111.01273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 15 of 15)

- [Splitting Methods for Convex Clustering](https://mlsanity.com/paper/1304.0499/) (2013)
- [On Graphical Models via Univariate Exponential Family Distributions](https://mlsanity.com/paper/1301.4183/) (2013)
- [Statistical Properties of Convex Clustering](https://mlsanity.com/paper/1503.08340/) (2015)
- [Nonparametric Bayes Modeling of Populations of Networks](https://mlsanity.com/paper/1406.7851/) (2014)
- [Recovering Trees with Convex Clustering](https://mlsanity.com/paper/1806.11096/) (2018)
- [Network Topology Inference with Graphon Spectral Penalties](https://mlsanity.com/paper/2010.07872/) (2020)
- [Convex Biclustering](https://mlsanity.com/paper/1408.0856/) (2014)
- [Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization](https://mlsanity.com/paper/1901.01477/) (2019)
- [On clustering network-valued data](https://mlsanity.com/paper/1606.02401/) (2016)
- [Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data](https://mlsanity.com/paper/1912.05449/) (2019)
- [Model-based clustering for populations of networks](https://mlsanity.com/paper/1806.00225/) (2018)
- [Splitting Methods for Convex Bi-Clustering and Co-Clustering](https://mlsanity.com/paper/1901.06075/) (2019)
- [Convex Hierarchical Clustering for Graph-Structured Data](https://mlsanity.com/paper/1911.03417/) (2019)
- [Automatic Registration and Clustering of Time Series](https://mlsanity.com/paper/2012.04756/) (2020)
- [Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering](https://mlsanity.com/paper/2012.04762/) (2020)

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

- [GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering](https://mlsanity.com/paper/2210.15721/) (2022)

## Abstract (arXiv)

Network models provide a powerful and flexible framework for analyzing a wide range of structured data sources. In many situations of interest, however, multiple networks can be constructed to capture different aspects of an underlying phenomenon or to capture changing behavior over time. In such settings, it is often useful to cluster together related networks in attempt to identify patterns of common structure. In this paper, we propose a convex approach for the task of network clustering. Our approach uses a convex fusion penalty to induce a smoothly-varying tree-like cluster structure, eliminating the need to select the number of clusters a priori. We provide an efficient algorithm for convex network clustering and demonstrate its effectiveness on synthetic examples.