Diss-l-ECT: Dissecting Graph Data with local Euler Characteristic Transforms

• URL: http://arxiv.org/abs/2410.02622v1
• Date: Thu, 3 Oct 2024 16:02:02 GMT
• Title: Diss-l-ECT: Dissecting Graph Data with local Euler Characteristic Transforms
• Authors: Julius von Rohrscheidt, Bastian Rieck,
• Abstract summary: We introduce the Local Euler Characteristic Transform ($ell$-ECT) to enhance expressivity and interpretability in graph representation learning. Unlike traditional Graph Neural Networks (GNNs), which may lose critical local details through aggregation, the $ell$-ECT provides a lossless representation of local neighborhoods. Our method exhibits superior performance than standard GNNs on a variety of node classification tasks, particularly in graphs with high heterophily.
• Score: 13.608942872770855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Euler Characteristic Transform (ECT) is an efficiently-computable geometrical-topological invariant that characterizes the global shape of data. In this paper, we introduce the Local Euler Characteristic Transform ($\ell$-ECT), a novel extension of the ECT particularly designed to enhance expressivity and interpretability in graph representation learning. Unlike traditional Graph Neural Networks (GNNs), which may lose critical local details through aggregation, the $\ell$-ECT provides a lossless representation of local neighborhoods. This approach addresses key limitations in GNNs by preserving nuanced local structures while maintaining global interpretability. Moreover, we construct a rotation-invariant metric based on $\ell$-ECTs for spatial alignment of data spaces. Our method exhibits superior performance than standard GNNs on a variety of node classification tasks, particularly in graphs with high heterophily.

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