AWhat changed
Offered graph-oriented message/aggregation/mutation abstractions across tensor backends, combining sparse graph handling, fusion and batching for graph neural-network workloads.
Scientific scope
Offered graph-oriented message/aggregation/mutation abstractions across tensor backends, combining sparse graph handling, fusion and batching for graph neural-network workloads. The record concerns the 2019 workshop system. Implementing graph models does not invent their methods or prove arbitrary graph-model expressivity. Performance depends on the selected operators, graph structure and tensor backend.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The record concerns the 2019 workshop system. Implementing graph models does not invent their methods or prove arbitrary graph-model expressivity. Performance depends on the selected operators, graph structure and tensor backend.
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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