Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched through interim discovery of node correspondences to more accurately find the next correspondences during the node matching. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of node pairs exhibiting strong consistency, which are easy to be discovered in the early stage of gradual matching. Specifically, Grad-Align first generates node embeddings of the two networks based on graph neural networks along with our layer-wise reconstruction loss, a loss built upon capturing the first-order and higher-order neighborhood structures. Then, nodes are gradually aligned by computing dual-perception similarity measures including the multi-layer embedding similarity as well as the Tversky similarity, an asymmetric set similarity using the Tversky index applicable to networks with different scales. Additionally, we incorporate an edge augmentation module into Grad-Align to reinforce the structural consistency. Through comprehensive experiments using real-world and synthetic datasets, we empirically demonstrate that Grad-Align consistently outperforms state-of-the-art NA methods.

翻译：网络对齐（NA）旨在基于网络结构和节点属性，寻找两个网络间节点的对应关系。本研究的动机源于现有大部分NA方法试图一次性发现所有节点对，因此在节点匹配过程中，未能充分利用中间阶段已发现的节点对应关系所蕴含的丰富信息，以更准确地寻找后续对应关系。为解决这一挑战，我们提出Grad-Align，一种新型NA方法，通过充分利用在渐进匹配早期阶段易于发现的具有强一致性的节点对，逐步发现节点对。具体而言，Grad-Align首先基于图神经网络结合我们的逐层重构损失（一种基于捕获一阶和高阶邻域结构的损失函数）生成两个网络的节点嵌入。随后，通过计算双重感知相似性度量（包括多层嵌入相似性以及Tversky相似性——一种适用于不同规模网络、利用Tversky指数的非对称集合相似性）逐步对齐节点。此外，我们在Grad-Align中融入边增强模块以强化结构一致性。通过使用真实世界和合成数据集进行的全面实验，我们实证证明Grad-Align在性能上持续优于最先进的NA方法。