On the Power of Gradual Network Alignment Using Dual-Perception Similarities

TL;DR

This paper introduces Grad-Align, a novel network alignment method that incrementally discovers node correspondences by leveraging dual-perception similarities and structural reinforcement, outperforming existing approaches.

Contribution

Grad-Align is the first to utilize a gradual, iterative matching process with dual-perception similarities and an edge augmentation module for improved network alignment accuracy.

Findings

Grad-Align outperforms state-of-the-art NA methods on real-world datasets.

The method effectively leverages incremental discovery of node pairs.

Edge augmentation enhances structural consistency and alignment accuracy.

Abstract

Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched through interim discovery of node correspondences to more accurately find the next correspondences during the node matching. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of node pairs exhibiting strong consistency, which are easy to be discovered in the early stage of gradual matching. Specifically, Grad-Align first generates node embeddings of the two networks based on graph neural networks along with our layer-wise reconstruction loss, a loss built upon capturing the first-order and higher-order neighborhood structures. Then, nodes are gradually aligned by computing dual-perception similarity measures including the multi-layer embedding similarity as well as the Tversky similarity, an asymmetric set similarity using the Tversky index applicable to networks with different scales. Additionally, we incorporate an edge augmentation module into Grad-Align to reinforce the structural consistency. Through comprehensive experiments using real-world and synthetic datasets, we empirically demonstrate that Grad-Align consistently outperforms state-of-the-art NA methods.