Simplifying Clustering with Graph Neural Networks
Machine Learning
2022-11-29 v2
Abstract
The objective functions used in spectral clustering are usually composed of two terms: i) a term that minimizes the local quadratic variation of the cluster assignments on the graph and; ii) a term that balances the clustering partition and helps avoiding degenerate solutions. This paper shows that a graph neural network, equipped with suitable message passing layers, can generate good cluster assignments by optimizing only a balancing term. Results on attributed graph datasets show the effectiveness of the proposed approach in terms of clustering performance and computation time.
Cite
@article{arxiv.2207.08779,
title = {Simplifying Clustering with Graph Neural Networks},
author = {Filippo Maria Bianchi},
journal= {arXiv preprint arXiv:2207.08779},
year = {2022}
}