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.

本研究提出了 Grad-Align，一种全新的网络对齐方法，通过渐进地发现具有强一致性的节点对来逐步对齐节点，实现了更加准确的节点匹配。在真实世界和合成数据集的综合实验中，我们证明了 Grad-Align 在网络对齐方面优于现有的最先进的方法。