论文标题
基于内存的图形网络
Memory-Based Graph Networks
论文作者
论文摘要
图形神经网络（GNN）是一类深层模型，这些模型在以图形为图的任意拓扑的数据上运行。我们为GNN引入了一个有效的存储层，该内存层可以共同学习节点表示并使图形更加粗糙。我们还基于此层介绍了两个新的网络：基于内存的GNN（MEMGNN）和图表内存网络（GMN），可以学习层次图表。实验结果表明，提出的模型实现了最先进的结果，其中九个图形分类和回归基准中的八个。我们还表明，学习的表示形式可以对应于分子数据中的化学特征。代码和参考实现的发布：https：//github.com/amirkhas/graphmemorynet
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
