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arXiv Machine Learning By Luca Sbicego, Xiaowen Dong, Dorina Thanou

Hierarchical Graph Representation Learning with Pooling-Induced Substructures

Read the original on arXiv Machine Learning →

The paper introduces Pooled-Substructure-Aware GNNs, a graph pooling method that explicitly preserves substructures identified by structure-based pooling, thereby improving the expressive power of pooling-based GNNs as measured by the Weisfeiler–Lehman test. The authors also present two synthetic datasets with controllable hierarchical structure and a metric for assessing hierarchical patterns in real-world graph-level benchmarks. Experiments show that the proposed approach outperforms matched pooling baselines on tasks with hierarchical structure while remaining competitive on simpler benchmarks.

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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