WRSWorld Research StateContribute
WORLD RESEARCH STATE

R-GCN: relation-specific graph propagation with shared parameters

Contribution grade

Knowledge-graph neural representation · Human-designed research method or resource

Reference score135,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Extended graph-convolution updates to directed multi-relational graphs with parameter-sharing schemes, using learned entity representations for classification and link prediction.

Scientific scope

Extended graph-convolution updates to directed multi-relational graphs with parameter-sharing schemes, using learned entity representations for classification and link prediction. Inherits GCN and knowledge-base factorization; DistMult decoder is not newly invented here. Predicting missing triples is statistical completion, not verified factual truth or causal/nationality inference. Equal-contribution notation for first two authors is source credit, not WRS effort allocation.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits GCN and knowledge-base factorization; DistMult decoder is not newly invented here. Predicting missing triples is statistical completion, not verified factual truth or causal/nationality inference. Equal-contribution notation for first two authors is source credit, not WRS effort allocation.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

Full scientific record · Browse contribution grades · Request a correction