RAM augments relational graph models with attribute-semantic retrieval via random-walk documents and two contrastive augmentations (ATRA, ETRA) to achieve state-of-the-art results on five real-world databases.
Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 5roles
background 1polarities
unclear 1representative citing papers
A differentiable neural model recovers ground-truth lifted action schemas from state traces by jointly learning schemas and inferring unobserved action arguments.
GNNs with ontology-derived semantic loss create hierarchy-aware box embeddings of a yeast knowledge graph that raise double-knockout growth prediction R² to 0.377 and generalize to triple knockouts while identifying a validated trait association.
Entity representations learned from text via link prediction generalize to unseen entities and transfer to classification and retrieval with reported gains of 22% MRR, 16% accuracy, and 8.8% NDCG@10.
Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.
citing papers explorer
-
From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics
RAM augments relational graph models with attribute-semantic retrieval via random-walk documents and two contrastive augmentations (ATRA, ETRA) to achieve state-of-the-art results on five real-world databases.
-
Differentiable Learning of Lifted Action Schemas for Classical Planning
A differentiable neural model recovers ground-truth lifted action schemas from state traces by jointly learning schemas and inferring unobserved action arguments.
-
Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
GNNs with ontology-derived semantic loss create hierarchy-aware box embeddings of a yeast knowledge graph that raise double-knockout growth prediction R² to 0.377 and generalize to triple knockouts while identifying a validated trait association.
-
Inductive Entity Representations from Text via Link Prediction
Entity representations learned from text via link prediction generalize to unseen entities and transfer to classification and retrieval with reported gains of 22% MRR, 16% accuracy, and 8.8% NDCG@10.
-
Explaining Graph Neural Networks for Node Similarity on Graphs
Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.