Abstract
Complex networks are frequently employed to model physical or virtual complex systems. When certain entities exist across multiple systems simultaneously, unveiling their corresponding relationships across the networks becomes crucial. This problem, known as network alignment, holds significant importance. It enhances our understanding of complex system structures and behaviours, facilitates the validation and extension of theoretical physics research about studying complex systems, and fosters diverse practical applications across various fields. However, due to variations in the structure, characteristics, and properties of complex networks across different fields, the study of network alignment is often isolated within each domain, with even the terminologies and concepts lacking uniformity. This review comprehensively summarizes the latest advancements in network alignment research, focusing on analyzing network alignment characteristics and progress in various domains such as social network analysis, bioinformatics, computational linguistics and privacy protection. It provides a detailed analysis of various methods' implementation principles, processes, and performance differences, including structure consistency-based methods, network embedding-based methods, and graph neural network-based (GNN-based) methods. Additionally, the methods for network alignment under different conditions, such as in attributed networks, heterogeneous networks, directed networks, and dynamic networks, are presented. Furthermore, the challenges and the open issues for future studies are also discussed.
Abstract (translated)
复杂网络经常被用来模拟物理或虚拟的复杂系统。当某些实体同时存在于多个系统中时，揭示这些实体在网络之间的对应关系变得至关重要。这一问题被称为网络对齐，在研究复杂系统的结构和行为、验证并拓展理论物理学关于复杂系统的研究以及促进跨不同领域的多种实际应用方面具有重要意义。然而，由于各领域中的复杂网络在结构、特征及属性上存在差异，网络对齐的研究往往局限于各自领域内进行，并且甚至术语和概念也缺乏统一性。 本文全面总结了最新的网络对齐研究进展，侧重于分析社交网络分析、生物信息学、计算语言学以及隐私保护等各领域的网络对齐特性与进展。详细探讨了不同方法的实施原理、过程及其性能差异，包括基于结构一致性的方法、基于网络嵌入的方法和基于图神经网络（GNN）的方法。此外，还介绍了在属性网络、异构网络、有向网络以及动态网络等不同条件下进行网络对齐的方法，并讨论了未来研究面临的挑战与开放性问题。
URL
https://arxiv.org/abs/2504.11367
