Simplifying Clustering with Graph Neural Networks

• URL: http://arxiv.org/abs/2207.08779v1
• Date: Mon, 18 Jul 2022 17:36:54 GMT
• Title: Simplifying Clustering with Graph Neural Networks
• Authors: Filippo Maria Bianchi
• Abstract summary: 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 time.
• Score: 5.571369922847262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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