AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
This addresses a longstanding issue in spectral graph clustering for researchers and practitioners by providing an automated solution with reliability guarantees, though it appears incremental as it builds on prior analysis.
The paper tackles the model order selection problem in spectral graph clustering by proposing AMOS, an automated algorithm that outputs clusters with statistical reliability guarantees and demonstrates superior performance on real-world datasets compared to three other methods.
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.