mlsanityData to 14 Oct 2026

Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction

arXiv 2105.13975 ↗First posted 28 May 2021cs.LG cs.AI stat.MLcited by 6

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Abstract (arXiv)

Sampling is an established technique to scale graph neural networks to large graphs. Current approaches however assume the graphs to be homogeneous in terms of relations and ignore relation types, critically important in biomedical graphs. Multi-relational graphs contain various types of relations that usually come with variable frequency and have different importance for the problem at hand. We propose an approach to modeling the importance of relation types for neighborhood sampling in graph neural networks and show that we can learn the right balance: relation-type probabilities that reflect both frequency and importance. Our experiments on drug-drug interaction prediction show that state-of-the-art graph neural networks profit from relation-dependent sampling in terms of both accuracy and efficiency.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 28 May 2021.

Builds on

18 of 32 references.

References · 18

  1. 2017Inductive Representation Learning on Large Graphscited by 6,320
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  4. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  6. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
  7. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  8. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
  9. 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 396
  10. 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
  11. 2019Learning Discrete Structures for Graph Neural Networkscited by 231
  12. 2018Adaptive Sampling Towards Fast Graph Representation Learningcited by 180
6 more
  1. 2018End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completioncited by 136
  2. 2019MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactionscited by 24
  3. 2019CASTER: Predicting Drug Interactions with Chemical Substructure Representationcited by 24
  4. 2019Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoningcited by 23
  5. 2018Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoderscited by 17
  6. 2019GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networkscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 0; 2025: 0; 2026: 1.

6 papers.

  1. 2022A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphscited by 49
  2. 2022AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoningcited by 28
  3. 2021A Unified View of Relational Deep Learning for Drug Pair Scoringcited by 7
  4. 2023Knowledge Graph Completion based on Tensor Decomposition for Disease Gene Predictioncited by 1
  5. 2022Multi-View Substructure Learning for Drug-Drug Interaction Predictioncited by 0
  6. 2026Scalable GNN-based Knowledge Graph Representation Learning with Efficient Message Passingcited by 0