Simplifying Clustering with Graph Neural Networks

Filippo Maria Bianchi

doi:10.7557/18.6790

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arXiv · 2207.08779

Simplifying Clustering with Graph Neural Networks

Filippo Maria Bianchi

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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.

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Filippo Maria Bianchi. 2022-07-18. Simplifying Clustering with Graph Neural Networks. https://doi.org/10.7557/18.6790

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