Attributed Network Representation Learning Based on Graph Neural Network: A Comprehensive Survey

ABSTRACT An attributed network encodes richer information through node and edge attributes. Attributed network representation learning (ANRL) seeks to obtain low‐dimensional node embeddings by jointly modeling structural topology and attribute semantics. Graph neural network (GNN)‐based methods, which leverage recursive message passing, have become the mainstream approach in this area. However, existing reviews provide limited systematic categorization and comparative analysis. In this paper, we classify existing GNN‐based attributed network embedding methods into six categories: graph convolution network (GCN)‐based methods, heterogeneous graph neural network‐based methods, graph autoencoder‐based methods, bidirectional encoder representations from transformers (BERT)‐based methods, hyper‐graph neural network (HGNN)‐based methods, and Bayesian graph neural network‐based methods. We not only summarize a large number of attributed net‐work embedding methods but also analyze and compare these methods. Additionally, we introduce some typical application scenarios in this field. Finally, we discuss the challenges and highlight several future research directions.