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Do Graph Neural Networks Learn Generalizable Algorithms for Clustering Graphs?

NeurIPS 2026 · Graph learning › Graph algorithms and clustering · approach: Neural and local graph clustering

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Studies whether GNNs trained to cluster graphs learn generalizable algorithms, focusing on a standard cut-based clustering cost function.

Problem

Unclear what algorithms GNNs learn for graph clustering and whether they generalize to unseen graphs that differ significantly from training data.

Approach

Examines GNNs for graph clustering under a standard cut-based clustering cost function, asking whether learned behavior corresponds to a generalizable algorithm rather than in-distribution fitting. Full method details unavailable from the abstract.

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