Node Feature Augmentation Vitaminizes Network Alignment

• URL: http://arxiv.org/abs/2304.12751v4
• Date: Fri, 17 May 2024 12:15:08 GMT
• Title: Node Feature Augmentation Vitaminizes Network Alignment
• Authors: Jin-Duk Park, Cong Tran, Won-Yong Shin, Xin Cao,
• Abstract summary: Network alignment (NA) is the task of discovering node correspondences across multiple networks. We propose Grad-Align+, a novel NA method built upon a recent state-of-the-art NA method, the so-called Grad-Align. Grad-Align+ consists of three key components: 1)-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)
• Score: 13.52901288497192
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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