Memory-Based Graph Networks
Abstract
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.
Cite
Text
Khasahmadi et al. "Memory-Based Graph Networks." International Conference on Learning Representations, 2020.Markdown
[Khasahmadi et al. "Memory-Based Graph Networks." International Conference on Learning Representations, 2020.](https://mlanthology.org/iclr/2020/khasahmadi2020iclr-memorybased/)BibTeX
@inproceedings{khasahmadi2020iclr-memorybased,
title = {{Memory-Based Graph Networks}},
author = {Khasahmadi, Amir Hosein and Hassani, Kaveh and Moradi, Parsa and Lee, Leo and Morris, Quaid},
booktitle = {International Conference on Learning Representations},
year = {2020},
url = {https://mlanthology.org/iclr/2020/khasahmadi2020iclr-memorybased/}
}