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arXiv Machine Learning By Francesco Paolo Nerini, Francesco Bonchi, Arijit Khan, Andr\'e Panisson

Inductive Correlation Clustering with Graph Neural Networks

Read the original on arXiv Machine Learning →

The paper introduces Inductive Correlation Clustering, a new framework that uses Graph Neural Networks to solve the Correlation Clustering problem on unseen graph instances. By learning common structural patterns and node features, the method generalizes to new graphs with minimal computational overhead, achieving inference times up to five orders of magnitude faster while maintaining an approximation ratio within about 10% of the best baseline. It also demonstrates competitive performance on standard transductive benchmarks and serves as an efficient learnable pooling layer for graph classification tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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