Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs. We introduce an efficient memory layer for GNNs that can jointly learn node representations and coarsen the graph. We also introduce two new networks based on this layer: memory-based GNN (MemGNN) and graph memory network (GMN) that can learn hierarchical graph representations. The experimental results shows that the proposed models achieve state-of-the-art results in eight out of nine graph classification and regression benchmarks. We also show that the learned representations could correspond to chemical features in the molecule data. Code and reference implementations are released at: https://github.com/amirkhas/GraphMemoryNet
@article{arxiv.2002.09518,
title = {Memory-Based Graph Networks},
author = {Amir Hosein Khasahmadi and Kaveh Hassani and Parsa Moradi and Leo Lee and Quaid Morris},
journal= {arXiv preprint arXiv:2002.09518},
year = {2020}
}