  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.
