  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.
