AWhat changed
Removed intermediate nonlinearities and collapsed feature transformations in GCN-style models, precomputing repeated graph propagation before fitting a linear classifier.
Scientific scope
Removed intermediate nonlinearities and collapsed feature transformations in GCN-style models, precomputing repeated graph propagation before fitting a linear classifier. Inherits GCN and graph low-pass filtering. Matching selected benchmarks does not establish that nonlinearities are unnecessary for all graph tasks or preserve full GCN expressivity.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits GCN and graph low-pass filtering. Matching selected benchmarks does not establish that nonlinearities are unnecessary for all graph tasks or preserve full GCN expressivity.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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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