Network alignment (NA) is the task of discovering node correspondences across different networks. Although NA methods have achieved remarkable success in a myriad of scenarios, their satisfactory performance is not without prior anchor link information and/or node attributes, which may not always be available. In this paper, we propose Grad-Align+, a novel NA method using node attribute augmentation that is quite robust to the absence of such additional information. Grad-Align+ is built upon a recent state-of-the-art NA method, the so-called Grad-Align, that gradually discovers only a part of node pairs until all node pairs are found. Specifically, Grad-Align+ is composed of the following key components: 1) augmenting node attributes based on nodes' centrality measures, 2) calculating an embedding similarity matrix extracted from a graph neural network into which the augmented node attributes are fed, and 3) gradually discovering node pairs by calculating similarities between cross-network nodes with respect to the aligned cross-network neighbor-pair. Experimental results demonstrate that Grad-Align+ exhibits (a) superiority over benchmark NA methods, (b) empirical validation of our theoretical findings, and (c) the effectiveness of our attribute augmentation module.
翻译:网络对齐 (NA) 是发现不同网络的节点通信的任务 。 虽然NA 方法在众多情况下都取得了显著的成功, 但它们的令人满意的性能并不是没有前锚链接信息和(或)节点属性, 而这些属性可能并不总是存在 。 在本文中, 我们提出使用节点属性增强的新型NA 方法Grad- Align+, 这是一种使用节点属性增强的新颖的NA 方法, 与缺少额外信息相当强 。 Grad- Align+ 建立在最新的最先进的NA 方法上, 即所谓的Grad- Align, 在找到所有节点配之前, 只能逐渐发现节点对配的一部分 。 具体来说, Grad- Align+ 由以下关键组成部分组成:1) 根据节点中心度测量增加节点属性, 2) 计算从图形神经网络中提取的嵌入的类似矩阵, 以输入增强节点属性属性的网络, 3) 通过计算匹配的跨网络节点与邻国对齐的交叉网络节点的相似性节点, 。 实验结果显示, Grad- Align+ 显示, 我们的理论比 。