论文标题
与节点边缘共进的异质图上的元图注意
Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution
论文作者
论文摘要
图神经网络已成为建模结构化数据的重要工具。在许多实际系统中，可能存在复杂的隐藏信息，例如，节点/边缘中的异质性，静态节点/边缘属性以及时空节点/边缘特征。但是，大多数现有方法仅考虑一部分信息。在本文中，我们介绍了共同进化的元图神经网络（COMGNN），该网络将元图注意应用于与节点和边缘状态共同进化的异质图。我们进一步提出了Comgnn（ST-COMGNN）的时空适应性，用于对节点和边缘的时空模式进行建模。我们在两个大型现实世界数据集上进行实验。实验结果表明，我们的模型大大优于最先进的方法，证明了从不同方面编码各种信息的有效性。
Graph neural networks have become an important tool for modeling structured data. In many real-world systems, intricate hidden information may exist, e.g., heterogeneity in nodes/edges, static node/edge attributes, and spatiotemporal node/edge features. However, most existing methods only take part of the information into consideration. In this paper, we present the Co-evolved Meta Graph Neural Network (CoMGNN), which applies meta graph attention to heterogeneous graphs with co-evolution of node and edge states. We further propose a spatiotemporal adaption of CoMGNN (ST-CoMGNN) for modeling spatiotemporal patterns on nodes and edges. We conduct experiments on two large-scale real-world datasets. Experimental results show that our models significantly outperform the state-of-the-art methods, demonstrating the effectiveness of encoding diverse information from different aspects.
