# Template-Based Graph Clustering

arXiv: https://arxiv.org/abs/2107.01994 · HTML: https://mlsanity.com/paper/2107.01994/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation](https://mlsanity.com/paper/1603.03236/) (2016)
- [A Nonlinear Orthogonal Non-Negative Matrix Factorization Approach to Subspace Clustering](https://mlsanity.com/paper/1709.10323/) (2017)

## Abstract (arXiv)

We propose a novel graph clustering method guided by additional information on the underlying structure of the clusters (or communities). The problem is formulated as the matching of a graph to a template with smaller dimension, hence matching n vertices of the observed graph (to be clustered) to the k vertices of a template graph, using its edges as support information, and relaxed on the set of orthonormal matrices in order to find a k dimensional embedding. With relevant priors that encode the density of the clusters and their relationships, our method outperforms classical methods, especially for challenging cases.