Network alignment (NA) is the task of discovering node correspondences across multiple networks. Although NA methods have achieved remarkable success in a myriad of scenarios, their effectiveness is not without additional information such as prior anchor links and/or node features, which may not always be available due to privacy concerns or access restrictions. To tackle this challenge, we propose Grad-Align+, a novel NA method built upon a recent state-of-the-art NA method, the so-called Grad-Align, that gradually discovers a part of node pairs until all node pairs are found. In designing Grad-Align+, we account for how to augment node features in the sense of performing the NA task and how to design our NA method by maximally exploiting the augmented node features. To achieve this goal, Grad-Align+ consists of three key components: 1) centrality-based node feature augmentation (CNFA), 2) graph neural network (GNN)-aided embedding similarity calculation alongside the augmented node features, and 3) gradual NA with similarity calculation using aligned cross-network neighbor-pairs (ACNs). Through comprehensive experiments, we demonstrate that Grad-Align+ exhibits (a) the superiority over benchmark NA methods, (b) empirical validations as well as our theoretical findings to see the effectiveness of CNFA, (c) the influence of each component, (d) the robustness to network noises, and (e) the computational efficiency.

翻译：网络对齐（NA）旨在发现跨多个网络的节点对应关系。尽管NA方法已在众多场景中取得显著成功，但其有效性离不开先验锚点链接和/或节点特征等附加信息，而这些信息可能因隐私问题或访问限制而不可用。为应对这一挑战，我们提出Grad-Align+，这是一种基于最新最先进NA方法Grad-Align的新型NA方法，该方法逐步发现部分节点对直至所有节点对被找到。在设计Grad-Align+时，我们考虑了如何在执行NA任务的意义上增强节点特征，以及如何通过最大化利用增强后的节点特征来设计NA方法。为实现这一目标，Grad-Align+包含三个关键组件：1）基于中心性的节点特征增强（CNFA），2）图神经网络（GNN）辅助的嵌入相似度计算（结合增强后的节点特征），以及3）利用对齐的跨网络邻居对（ACNs）进行相似度计算的渐进式NA。通过全面实验，我们证明了Grad-Align+具有：(a) 优于基准NA方法的表现，(b) 验证CNFA有效性的实证结果及理论发现，(c) 各组件的各自影响，(d) 对网络噪声的鲁棒性，以及(e) 计算效率。