Inductive Correlation Clustering with Graph Neural Networks
Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and does not require a pre-specified number of clusters. Given $n$ objects and a pairwise similarity function, the goal is to cluster the objects so that similar objects are put in the same cluster and dissimilar objects are put in different clusters. Despite its versatility, existing CC algorithms suffer from significant scalability issues and are inherently transductive: i.e., the algorithm must be executed from scratch for any new problem instance. In this work, we bridge this gap by leveraging Graph Neural Networks (GNNs) to solve Inductive C...
| Authors | Francesco Paolo Nerini, Francesco Bonchi, Arijit Khan et al. |
|---|---|
| Primary category | cs.LG |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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