Memory-Based Graph Networks
This work addresses the challenge of improving graph learning for tasks like classification and regression, with applications such as molecule analysis, though it appears incremental as it builds on existing GNN frameworks.
The paper tackles the problem of learning hierarchical graph representations by introducing an efficient memory layer for graph neural networks (GNNs) that jointly learns node representations and coarsens the graph, resulting in state-of-the-art performance in eight out of nine graph classification and regression benchmarks.
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