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

## TL;DR

MR-GNN introduces a multi-resolution, dual graph neural network architecture that effectively captures diverse substructure features and interactions between structured entities, advancing predictive accuracy in complex tasks.

## Contribution

It proposes a novel multi-resolution and dual graph-state LSTM architecture to better model interactions between structured entities in graphs.

## Key findings

- MR-GNN outperforms existing methods on real-world datasets.
- The multi-resolution approach captures diverse substructure features.
- Dual graph-state LSTMs effectively model entity interactions.

## Abstract

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.