Network alignment is the task of establishing one-to-one correspondences between the nodes of different graphs and finds a plethora of applications in high-impact domains. However, this task is known to be NP-hard in its general form, and existing algorithms do not scale up as the size of the graphs increases. To tackle both challenges we propose a novel generalized graph autoencoder architecture, designed to extract powerful and robust node embeddings, that are tailored to the alignment task. We prove that the generated embeddings are associated with the eigenvalues and eigenvectors of the graphs and can achieve more accurate alignment compared to classical spectral methods. Our proposed framework also leverages transfer learning and data augmentation to achieve efficient network alignment at a very large scale without retraining. Extensive experiments on both network and sub-network alignment with real-world graphs provide corroborating evidence supporting the effectiveness and scalability of the proposed approach.

翻译：网络对齐是在不同图的节点之间建立一一对应关系的任务，在多个高影响力领域具有广泛应用。然而，该任务在一般形式下被证明是NP难的，且现有算法无法随图规模的扩大而有效扩展。为应对这两项挑战，我们提出了一种新型广义图自编码器架构，旨在提取适用于对齐任务的强大且鲁棒的节点嵌入。我们证明，生成的嵌入与图的特征值和特征向量相关联，能够比经典谱方法实现更精确的对齐。所提出的框架还利用迁移学习和数据增强，无需重新训练即可实现超大规模网络的高效对齐。在真实世界图上的网络对齐和子网络对齐实验均提供了支持性证据，验证了所提方法的有效性和可扩展性。