Author

J. Lutzeyer

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#machine learning Sep 2025

SHAKE-GNN: Scalable Hierarchical Kirchhoff-Forest Graph Neural Network

This work introduces SHAKE-GNN, a novel scalable graph-level GNN framework based on a hierarchy of Kirchhoff Forests, a class of random spanning forests used to construct stochastic multi-resolution decompositions of graphs, enabling flexible trade-offs between efficiency and performance.

Zhipu Cui, J. Lutzeyer · arXiv.org · 0 citations

#machine learning May 2025

Graph Representational Learning: When Does More Expressivity Hurt Generalization?

A family of premetrics that capture different degrees of structural similarity between graphs are introduced and relate these similarities to generalization, and consequently, the performance of expressive GNNs are related.

Sohir Maskey, Raffaele Paolino, Fabian Jogl et al. · arXiv.org · 4 citations · ⚡1