AWhat changed
Adds degree centrality, shortest-path distance attention biases and path-based edge encodings to a Transformer graph representation model.
Scientific scope
Adds degree centrality, shortest-path distance attention biases and path-based edge encodings to a Transformer graph representation model. Eight original authors; internship/correspondence marks are not equal-contribution marks. Source-specific expressivity and benchmark statements do not establish all-graph universality or linear-time attention. Path selection and graph preprocessing have costs; the architectural contribution is distinct from independently established application outcomes.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Eight original authors; internship/correspondence marks are not equal-contribution marks. Source-specific expressivity and benchmark statements do not establish all-graph universality or linear-time attention. Path selection and graph preprocessing have costs; the architectural contribution is distinct from independently established application outcomes.
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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