基于中心性的节点特征增强用于鲁棒网络对齐
Centrality-Based Node Feature Augmentation for Robust Network Alignment
AI总结：
提出Grad-Align+，基于中心性特征增强和GNN的渐进网络对齐方法，在无额外信息下优于基准，且对噪声鲁棒、计算高效。
摘要：
网络对齐（NA）是在多个网络中发现节点对应关系的任务。尽管NA方法在众多场景中取得了显著成功，但其有效性依赖于额外信息，如先验锚链接和/或节点特征，而这些信息由于隐私问题或访问限制可能并不总是可用。为应对这一挑战，我们提出了Grad-Align+，一种新颖的NA方法，它基于最近的最先进的NA方法（即Grad-Align），该方法逐步发现部分节点对，直到所有节点对都被找到。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我们的NA方法。为实现这一目标，Grad-Align+由三个关键组件组成：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以及增强后的节点特征，3）使用对齐的跨网络邻居对（ACNs）进行相似度计算的渐进式NA。通过全面的实验，我们证明Grad-Align+表现出（a）相对于基准NA方法的优越性，（b）实证验证以及我们的理论发现以证明CNFA的有效性，（c）每个组件的影响，（d）对网络噪声的鲁棒性，以及（e）计算效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
