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E(n)-equivariant graph neural networks

Contribution grade

Graph and geometric machine learning · Human-designed research method or resource

Reference score155,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Uses invariant distance-conditioned scalar messages and radial coordinate updates to preserve Euclidean rotation, reflection and translation equivariance without computing higher-order spherical-harmonic features.

Scientific scope

Uses invariant distance-conditioned scalar messages and radial coordinate updates to preserve Euclidean rotation, reflection and translation equivariance without computing higher-order spherical-harmonic features. Equivariance is structural for the specified updates and invariant input features, not universal expressivity across all tensor representations, exact physical conservation or arbitrary graph-inference scalability. General architecture and artificial particle dynamics only; bio applications omitted.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Equivariance is structural for the specified updates and invariant input features, not universal expressivity across all tensor representations, exact physical conservation or arbitrary graph-inference scalability. General architecture and artificial particle dynamics only; bio applications omitted.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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