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      "statement": "# R-GCN: relation-specific graph propagation with shared parameters\n\nExtended graph-convolution updates to directed multi-relational graphs with parameter-sharing schemes, using learned entity representations for classification and link prediction.\n\n## Scientific scope\n\nInherits 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.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nMichael Schlichtkrull; Thomas N. Kipf; Peter Bloem; Rianne van den Berg; Ivan Titov; Max Welling\n\nSource-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.\n\n\n\n## Original sources\n\n- [Modeling Relational Data with Graph Convolutional Networks](https://arxiv.org/abs/1703.06103v1)\n",
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