Graph Representation Learning in Complex Networks: Recent Advances and Open Challenges

Le résumé fourni par la source

Complex networks have been widely adopted to model diverse real-world systems. However, the increasing complexity of such networks, characterized by heterogeneity, higher-order interactions, and temporal and dynamic learning settings, poses critical challenges to traditional machine learning methods. To address these challenges, graph representation learning (GRL) has emerged as a fundamental paradigm for analyzing complex networks by learning low-dimensional representations that preserve structural and semantic information in non-Euclidean spaces. With the rapid development of deep learning, graph neural networks (GNNs) have become central to GRL, enabling end-to-end learning on graph-structured data. In this survey, we provide a systematic and comprehensive overview of graph representation learning in complex networks. We review representation learning techniques at multiple granularities, including node-level, edge-level, and graph-level embeddings. In addition, we summarize key aspects of advanced GRL research, encompassing GNN architectural design, structural complexity modeling, learning and optimization strategies, as well as representative real-world applications. Finally, we highlight emerging topics and open challenges to outline promising directions for future research.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.