Graph alignment, also known as network alignment has many applications in data mining tasks. It aims to find the node correspondence across disjoint graphs. Recently, various methods like representation learning methods, spectral methods have been proposed to solve the graph alignment problem, but they either only consider the local structure information but ignore the neighborhood similarity, or their alignment process is easy to be disturbed by nodes with similar structure or attribute. In this paper, we consider both center and neighborhood similarities, aiming to reduce the inconsistency between them and enlarge the difference among node representations. We further propose model DGAN(Deep Graph Alignment Network) containing the DNN module and GCN module to learn more unique node representations under the guidance of the attribute-supervised module. Moreover, we theoretically prove that most spectral methods can be unified into a linear GCN model. By extensive experiments on public benchmarks, we show that our model achieves a good balance between alignment accuracy and speed over multiple datasets compared with existing methods.

图对齐，也称为网络对齐，在数据挖掘任务中有很多应用。它旨在找到跨不相交图的节点对应关系。近年来，人们提出了表征学习方法、谱方法等各种方法来解决图对齐问题，但它们要么只考虑局部结构信息而忽略邻域相似性，要么它们的对齐过程容易受到结构相似节点的干扰。或属性。在本文中，我们考虑了中心和邻域的相似性，旨在减少它们之间的不一致并扩大节点表示之间的差异。我们进一步建议模型DGAN（d EEP ģ拍摄和甲lignment Ñ网络）包含 DNN 模块和 GCN 模块，以在属性监督模块的指导下学习更多独特的节点表示。此外，我们从理论上证明了大多数谱方法可以统一为线性 GCN 模型。通过对公共基准的大量实验，我们表明与现有方法相比，我们的模型在多个数据集上实现了对齐精度和速度之间的良好平衡。