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.

中文翻译：

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使用图神经网络简化聚类

谱聚类中使用的目标函数通常由两个项组成： i) 使图上聚类分配的局部二次变化最小化的项；以及；ii) 一个平衡聚类划分并有助于避免退化解决方案的术语。本文表明，配备合适的消息传递层的图神经网络可以通过仅优化平衡项来生成良好的集群分配。属性图数据集的结果显示了所提出的方法在聚类性能和计算时间方面的有效性。