Abstract
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.
Abstract (translated)
图形神经网络（GNNs）在非欧氏数据（如图形和网络）领域已经颠覆了机器学习。GNNs通过聚类和实现节点表示学习有效地实现了节点表示学习，并在许多图形相关任务中取得了令人印象深刻的成果。然而，大多数聚类方法是基于求和的，这可能会有问题，因为他们可能不足以编码有用的图形结构。此外，尽管图形池化模块对于图形学习（尤其是图形分类）也非常重要，但关于图形 down-sampling机制的研究仍然相当有限。为了应对上述挑战，我们提出了一个基于连接的图形卷积机制，通过注入式更新节点表示以最大化区分类别的 discriminative power。此外，我们还设计了一个名为WL-SortPool的新颖图形池化模块，以在深度学习的方式学习中学习重要的子图模式。WL-SortPool对节点表示（即连续的WL颜色）进行层间排序，以分别学习具有不同深度的子树之间的相对重要性，从而更好地描述复杂的图形拓扑结构和图中所编码的丰富信息。我们提出了一个包含这些增强的全新的子图模式图形神经网络（SPGNN）架构。我们在许多图形分类基准上测试了所提出的SPGNN架构。实验结果表明，我们的方法可以与最先进的图形核和其他GNN方法一样实现高度竞争性的结果。
URL
https://arxiv.org/abs/2404.13655
