AWhat changed
Learns direct-edge and neighborhood-context embeddings using negative sampling and weight-proportional edge sampling, then combines separately trained proximity representations.
Scientific scope
Learns direct-edge and neighborhood-context embeddings using negative sampling and weight-proportional edge sampling, then combines separately trained proximity representations. Six source authors; internship mark not shared-lead mark. First-order objective applies to undirected graphs; second-order supports directed graphs. Practical method concatenates separately trained embeddings rather than solving a claimed joint objective. Empirical near-linear work is not a universal convergence/runtime theorem; isolated unseen vertices need extra information.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Six source authors; internship mark not shared-lead mark. First-order objective applies to undirected graphs; second-order supports directed graphs. Practical method concatenates separately trained embeddings rather than solving a claimed joint objective. Empirical near-linear work is not a universal convergence/runtime theorem; isolated unseen vertices need extra information.
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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