Memory-Based Graph Networks

Understand

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.