基于超图的动态图节点分类
Hypergraph-Based Dynamic Graph Node Classification
AI总结：
本文提出HYDG模型，通过个体级和群体级超图分别捕捉动态图中节点的时空表示与同类节点的多粒度时序依赖，结合超图神经网络加权聚合，在五个真实数据集上验证了其优于现有动态图节点分类方法。
摘要：
静态图上的节点分类已取得显著成功，但在节点拓扑、属性和标签随时间变化的动态图上实现准确的节点分类尚未得到很好的解决。现有基于RNN和自注意力的方法仅聚合同一节点在不同时间切片上的特征，无法充分处理和捕捉动态图中的多样化动态变化。因此，我们提出了一种新模型，名为基于超图的多粒度动态图节点分类（HYDG）。在通过GNN骨干网络获得每个切片的基本节点表示后，HYDG通过两个模块对动态图中每个节点的表示进行建模。个体级超图捕捉个体节点之间的时空节点表示，而群体级超图捕捉同一类别节点之间的多粒度群体时序表示。每个超边通过在特定时间范围内连接多个节点来捕捉不同长度的时序依赖关系。通过超图神经网络的加权信息传播和聚合获得更准确的表示。在五个真实动态图数据集上使用两种GNN骨干网络进行的大量实验证明了我们所提出框架的优越性。
英文摘要：
Node classification on static graphs has achieved significant success, but achieving accurate node classification on dynamic graphs where node topology, attributes, and labels change over time has not been well addressed. Existing methods based on RNNs and self-attention only aggregate features of the same node across different time slices, which cannot adequately address and capture the diverse dynamic changes in dynamic graphs. Therefore, we propose a novel model named Hypergraph-Based Multi-granularity Dynamic Graph Node Classification (HYDG). After obtaining basic node representations for each slice through a GNN backbone, HYDG models the representations of each node in the dynamic graph through two modules. The individual-level hypergraph captures the spatio-temporal node representations between individual nodes, while the group-level hypergraph captures the multi-granularity group temporal representations among nodes of the same class. Each hyperedge captures different temporal dependencies of varying lengths by connecting multiple nodes within specific time ranges. More accurate representations are obtained through weighted information propagation and aggregation by the hypergraph neural network. Extensive experiments on five real dynamic graph datasets using two GNN backbones demonstrate the superiority of our proposed framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
