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Attributed network representation learning (ANRL) seeks to obtain low\u2010dimensional node embeddings by jointly modeling structural topology and attribute semantics. Graph neural network (GNN)\u2010based 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\u2010based attributed network embedding methods into six categories: graph convolution network (GCN)\u2010based methods, heterogeneous graph neural network\u2010based methods, graph autoencoder\u2010based methods, bidirectional encoder representations from transformers (BERT)\u2010based methods, hyper\u2010graph neural network (HGNN)\u2010based methods, and Bayesian graph neural network\u2010based methods. We not only summarize a large number of attributed net\u2010work embedding methods but also analyze and compare these methods. 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