# MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

arXiv: https://arxiv.org/abs/1905.09558 · HTML: https://mlsanity.com/paper/1905.09558/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 24 of 24)

- [SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design](https://mlsanity.com/paper/2307.11694/) (2023)
- [ChemicalX: A Deep Learning Library for Drug Pair Scoring](https://mlsanity.com/paper/2202.05240/) (2022)
- [ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification](https://mlsanity.com/paper/2204.04618/) (2022)
- [Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction](https://mlsanity.com/paper/2306.05257/) (2023)
- [A Unified View of Relational Deep Learning for Drug Pair Scoring](https://mlsanity.com/paper/2111.02916/) (2021)
- [Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2105.13975/) (2021)
- [MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy](https://mlsanity.com/paper/2110.15087/) (2021)
- [Distinguish Confusion in Legal Judgment Prediction via Revised Relation Knowledge](https://mlsanity.com/paper/2408.09422/) (2024)
- [Mitigating cold start problems in drug-target affinity prediction with interaction knowledge transferring](https://mlsanity.com/paper/2202.01195/) (2022)
- [Understanding Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/2203.16060/) (2022)
- [FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning](https://mlsanity.com/paper/2410.15010/) (2024)
- [Recent Developments in GNNs for Drug Discovery](https://mlsanity.com/paper/2506.01302/) (2025)
- [Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations](https://mlsanity.com/paper/2507.09173/) (2025)
- [IA-GCN: Interactive Graph Convolutional Network for Recommendation](https://mlsanity.com/paper/2204.03827/) (2022)
- [Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning](https://mlsanity.com/paper/2301.05931/) (2023)
- [Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks](https://mlsanity.com/paper/2106.13061/) (2021)
- [Modeling Polypharmacy and Predicting Drug-Drug Interactions using Deep Generative Models on Multimodal Graphs](https://mlsanity.com/paper/2302.08680/) (2023)
- [Thermodynamics-informed graph neural networks for real-time simulation of digital human twins](https://mlsanity.com/paper/2412.12034/) (2024)
- [Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions](https://mlsanity.com/paper/2112.07837/) (2021)
- [Multi-View Substructure Learning for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2203.14513/) (2022)
- [Curvature-enhanced Graph Convolutional Network for Biomolecular Interaction Prediction](https://mlsanity.com/paper/2306.13699/) (2023)
- [Cooperative Minibatching in Graph Neural Networks](https://mlsanity.com/paper/2310.12403/) (2023)
- [Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions](https://mlsanity.com/paper/2402.18127/) (2024)
- [DPASyn: Mechanism-Aware Drug Synergy Prediction via Dual Attention and Precision-Aware Quantization](https://mlsanity.com/paper/2505.19144/) (2025)

## Abstract (arXiv)

Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods.