AWhat changed
Used tunable second-order random walks to sample diverse node neighborhoods and optimized a skip-gram style objective for continuous network embeddings.
Scientific scope
Used tunable second-order random walks to sample diverse node neighborhoods and optimized a skip-gram style objective for continuous network embeddings. Extends DeepWalk/word-vector objectives; does not invent random walks or all graph embeddings. Walk bias trades between sampled neighborhood structures, not a guarantee of exact structural equivalence or arbitrary unseen-graph inductive transfer. General ML method only; biological examples are not separately curated.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Extends DeepWalk/word-vector objectives; does not invent random walks or all graph embeddings. Walk bias trades between sampled neighborhood structures, not a guarantee of exact structural equivalence or arbitrary unseen-graph inductive transfer. General ML method only; biological examples are not separately curated.
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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