arXiv Machine Learning November 5, 2026 By Xiaoyang Lin, Renchi Yang, Yuqi Chen, Xin Huang

Alleviating Limited Effectiveness and Multi-Class Collapse in Deep Graph-level Clustering

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The paper introduces S$^3$GLC, a deep graph-level clustering framework that combines global topological features—derived via a Spectral Signature based on the Hoffman-Wielandt inequality—with local neighborhood information to improve clustering of graph datasets. It also incorporates a joint manifold-preserving sparse regression module to fuse these representations while reducing noise, and a balanced clustering objective using optimal transport to prevent clustering collapse, especially in imbalanced multi-class settings. Experiments on ten real-world datasets, including REDDIT-12K and OGBG-PPA, show that S$^3$GLC outperforms fifteen state-of-the-art baselines.

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