A research team led by Professor Dan Feng from Huazhong University of Science and Technology published a new study on 15 June 2026 in Frontiers of Computer Science, co-published by Higher Education Press and Springer Nature, addressing challenges in Social Network Alignment (SNA) such as sparse connections, heterogeneous structures, and dynamic changes. The team conducted a systematic review of SNA methods based on Graph Representation Learning (GRL), with a special focus on emerging approaches that integrate Large Language Models (LLMs) like Qwen, Llama2 and ERNIE to enhance semantic reasoning and alignment accuracy. They present a unified perspective on SNA methods covering static and dynamic networks as well as homogeneous and heterogeneous structures, proposing a comprehensive taxonomy from early matrix factorization techniques to deep graph neural networks. The study provides a benchmarking analysis across more than ten real-world datasets, comparing the effectiveness of various SNA methods. Future work can focus on developing more interpretable, scalable, and privacy-preserving SNA methods, with potential in combining GRL with lightweight or distilled LLMs to reduce computational costs while maintaining high alignment accuracy.