Graph neural networks (GNNs) have revolutionized the field of machine learning on non-Euclidean data such as graphs and networks. GNNs effectively implement node representation learning through neighborhood aggregation and achieve impressive results in many graph-related tasks. However, most neighborhood aggregation approaches are summation-based, which can be problematic as they may not be sufficiently expressive to encode informative graph structures. Furthermore, though the graph pooling module is also of vital importance for graph learning, especially for the task of graph classification, research on graph down-sampling mechanisms is rather limited. To address the above challenges, we propose a concatenation-based graph convolution mechanism that injectively updates node representations to maximize the discriminative power in distinguishing non-isomorphic subgraphs. In addition, we design a novel graph pooling module, called WL-SortPool, to learn important subgraph patterns in a deep-learning manner. WL-SortPool layer-wise sorts node representations (i.e. continuous WL colors) to separately learn the relative importance of subtrees with different depths for the purpose of classification, thus better characterizing the complex graph topology and rich information encoded in the graph. We propose a novel Subgraph Pattern GNN (SPGNN) architecture that incorporates these enhancements. We test the proposed SPGNN architecture on many graph classification benchmarks. Experimental results show that our method can achieve highly competitive results with state-of-the-art graph kernels and other GNN approaches.

翻译：图神经网络（GNN）彻底改变了在非欧几里得数据（如图与网络）上的机器学习领域。GNN通过邻域聚合有效实现节点表示学习，并在许多图相关任务中取得了显著成果。然而，大多数邻域聚合方法基于求和，这可能导致其表达力不足以编码信息丰富的图结构。此外，尽管图池化模块对图学习（尤其是图分类任务）至关重要，但关于图下采样机制的研究相对有限。为解决上述挑战，我们提出一种基于拼接的图卷积机制，该机制通过注入式地更新节点表示，以最大化区分非同构子图的判别能力。同时，我们设计了一种新颖的图池化模块——WL-SortPool，以深度学习方式学习重要子图模式。WL-SortPool逐层对节点表示（即连续WL颜色）进行排序，从而分别学习不同深度子树的相对重要性以辅助分类，进而更好地刻画复杂图拓扑结构及图中编码的丰富信息。我们提出一种融合上述增强的图神经网络架构——子图模式GNN（SPGNN）。我们在多个图分类基准数据集上测试了所提出的SPGNN架构。实验结果表明，我们的方法能够与最先进的图核及其他GNN方法取得极具竞争力的结果。