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

• URL: http://arxiv.org/abs/2208.11025v2
• Date: Wed, 24 Aug 2022 07:20:35 GMT
• Title: Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation
• Authors: Jin-Duk Park, Cong Tran, Won-Yong Shin, Xin Cao
• Abstract summary: Network alignment (NA) is the task of discovering node correspondences across different networks. We propose Grad-Align+, a novel NA method using node attribute augmentation. We show 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.
• Score: 4.536868213405015
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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