Node classification is to predict the class label of a node by analyzing its properties and interactions in a network. We note that many existing solutions for graph-based node classification only consider node connectivity but not the node’s local topology structure. However, nodes residing in different parts of a real-world network may share similar local topology structures. For example, local topology structures in a payment network may reveal sellers’ business roles (e.g., supplier or retailer). To model both connectivity and local topology structure for better node classification performance, we present DP-GCN, a dual-path graph convolution network. DP-GCN consists of three main modules: (i) a C-GCN module to capture the connectivity relationships between nodes, (ii) a T-GCN module to capture the topology structure similarity among nodes, and (iii) a multi-head self-attention module to align both properties. We evaluate DP-GCN on seven benchmark datasets against diverse baselines to demonstrate its effectiveness. We also provide a case study of running DP-GCN on three large-scale payment networks from PayPal, a leading payment service provider, for risky seller detection. Experimental results show DP-GCN’s effectiveness and practicability in large-scale settings. PayPal’s internal testing also shows DP-GCN’s effectiveness in defending against real risks from transaction networks.

节点分类是通过分析节点的属性和在网络中的交互来预测节点的类标签。我们注意到，许多现有的基于图的节点分类解决方案仅考虑节点的连通性，而不考虑节点的局部拓扑结构。然而，位于现实世界网络不同部分的节点可能共享相似的本地拓扑结构。例如，支付网络中的本地拓扑结构可以揭示卖家的业务角色（例如，供应商或零售商）。为了对连通性和局部拓扑结构进行建模以获得更好的节点分类性能，我们提出了 DP-GCN，一种双路径图卷积网络。 DP-GCN 由三个主要模块组成：(i) C-GCN 模块，用于捕获节点之间的连接关系；(ii) T-GCN 模块，用于捕获节点之间的拓扑结构相似性；(iii) 多头模块自注意力模块来调整这两个属性。我们根据不同的基线在七个基准数据集上评估 DP-GCN，以证明其有效性。我们还提供了在领先支付服务提供商 PayPal 的三个大型支付网络上运行 DP-GCN 的案例研究，用于风险卖家检测。实验结果证明了DP-GCN在大规模环境中的有效性和实用性。 PayPal的内部测试也显示了DP-GCN在防御交易网络真实风险方面的有效性。