Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation

TL;DR

Grad-Align+ is a robust network alignment method that uses attribute augmentation based on centrality measures to improve node matching accuracy, especially when prior anchor links or attributes are limited.

Contribution

It introduces a novel attribute augmentation technique and extends Grad-Align to enhance network alignment performance without relying heavily on prior information.

Findings

Outperforms benchmark NA methods in experiments.

Validates theoretical insights empirically.

Demonstrates effectiveness of attribute augmentation module.

Abstract

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.