arXiv Machine Learning June 2, 2026 By Junru Zhou, Cai Zhou, Xiyuan Wang, Pan Li, Muhan Zhang

Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors

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arXiv:2410. 09737v2 Announce Type: replace Abstract: A popular way to improve the expressive power of graph neural networks (GNNs) is to use Laplacian eigenvectors as additional node features, since they can serve both as structural identifiers and global coordinates of nodes.

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arXiv:2605.06462v2 Announce Type: replace Abstract: Progress in graph learning is hindered by benchmark practices that conflate the contributions of node features and graph structure, making it hard...

By Richard von Moos, Mathieu Alain, Bastian Rieck

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