AWhat changed
Used sparse gather/scatter kernels, a message-passing interface and block-diagonal batching of variable-size graphs to integrate geometric deep-learning methods with PyTorch.
Scientific scope
Used sparse gather/scatter kernels, a message-passing interface and block-diagonal batching of variable-size graphs to integrate geometric deep-learning methods with PyTorch. Implements inherited graph/point-cloud operators rather than inventing GCN, GraphSAGE, GAT or their theory. Speed and memory comparisons concern chosen setups; general irregular-data support is not arbitrary geometric invariance.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Implements inherited graph/point-cloud operators rather than inventing GCN, GraphSAGE, GAT or their theory. Speed and memory comparisons concern chosen setups; general irregular-data support is not arbitrary geometric invariance.
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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