论文标题
一个新颖的高阶Weisfeiler-Lehman图形卷积
A Novel Higher-order Weisfeiler-Lehman Graph Convolution
论文作者
论文摘要
当前的GNN体系结构使用顶点邻域聚合方案，该方案将其判别能力限制在一维Weisfeiler-Lehman（WL）图同构测试中。在这里，我们提出了一个基于二维WL检验的新型图卷积运算符。我们正式表明，所得的2-WL-GNN体系结构比现有的GNN方法更具歧视性。实验研究使用合成和实际数据来补充这一理论结果。在多个常见的图形分类基准上，我们证明了所提出的模型与最新的图形内核和GNN具有竞争力。
Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph convolution operator that is based on the 2-dimensional WL test. We formally show that the resulting 2-WL-GNN architecture is more discriminative than existing GNN approaches. This theoretical result is complemented by experimental studies using synthetic and real data. On multiple common graph classification benchmarks, we demonstrate that the proposed model is competitive with state-of-the-art graph kernels and GNNs.
