arXiv Machine Learning August 4, 2026 By Ali Parviz, Yuichi Yoshida

Nonlinear Laplacians Improve Signed-Directed Graph Learning

Read the original on arXiv Machine Learning →

arXiv:2608. 00836v1 Announce Type: new Abstract: While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian

Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs

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Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing

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