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.

本文提出了一种基于节点属性增强的网络对齐方法 Grad-Align+，其采用图神经网络计算嵌入相似性矩阵，并逐步发现跨网络节点对，实验结果表明该方法在节点属性缺失情况下表现优异，并有效增强了方法的鲁棒性。