# AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering

arXiv: https://arxiv.org/abs/1609.06457 · HTML: https://mlsanity.com/paper/1609.06457/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2016.

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

- [Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction](https://mlsanity.com/paper/2112.09746/) (2021)

## Abstract (arXiv)

One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. In this paper, we propose AMOS, an automated model order selection algorithm for SGC. Based on a recent analysis of clustering reliability for SGC under the random interconnection model, AMOS works by incrementally increasing the number of clusters, estimating the quality of identified clusters, and providing a series of clustering reliability tests. Consequently, AMOS outputs clusters of minimal model order with statistical clustering reliability guarantees. Comparing to three other automated graph clustering methods on real-world datasets, AMOS shows superior performance in terms of multiple external and internal clustering metrics.