论文标题
朝着表现力表示
Towards Expressive Graph Representation
论文作者
论文摘要
图形神经网络（GNN）将每个节点的邻域汇总到节点嵌入中，并显示出图形表示学习的强大能力。但是，大多数现有的GNN变体都以固定的非注射方式汇总了邻域信息，该方式可能会将不同的图或节点映射到相同的嵌入中，从而降低了模型的表现力。我们提出了一个理论框架，用于设计GNN中邻域聚集的连续注射集函数。使用框架，我们提出了表达GNN，该gnn通过连续的注射集合函数聚集了每个节点的邻域，以便GNN层映射具有与相似嵌入的相似邻域的相似节点，不同嵌入的不同节点，与不同的嵌入式和等效的节点或等值图或同一胚胎。此外，所提出的表达GNN可以自然地学习具有连续节点属性的图形的表达式表示。我们在多个基准数据集上验证了提出的表达GNN（EXPGNN），包括简单的图和属性图。实验结果表明，我们的模型在大多数基准上都实现了最先进的性能。
Graph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning. However, most existing GNN variants aggregate the neighborhood information in a fixed non-injective fashion, which may map different graphs or nodes to the same embedding, reducing the model expressiveness. We present a theoretical framework to design a continuous injective set function for neighborhood aggregation in GNN. Using the framework, we propose expressive GNN that aggregates the neighborhood of each node with a continuous injective set function, so that a GNN layer maps similar nodes with similar neighborhoods to similar embeddings, different nodes to different embeddings and the equivalent nodes or isomorphic graphs to the same embeddings. Moreover, the proposed expressive GNN can naturally learn expressive representations for graphs with continuous node attributes. We validate the proposed expressive GNN (ExpGNN) for graph classification on multiple benchmark datasets including simple graphs and attributed graphs. The experimental results demonstrate that our model achieves state-of-the-art performances on most of the benchmarks.
