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Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding
2021
Applied Sciences
As most networks come with some content in each node, attributed network embedding has aroused much research interest. Most existing attributed network embedding methods aim at learning a fixed representation for each node encoding its local proximity. However, those methods usually neglect the global information between nodes distant from each other and distribution of the latent codes. We propose Structural Adversarial Variational Graph Auto-Encoder (SAVGAE), a novel framework which encodes
doi:10.3390/app11052371
fatcat:lww3aaqmgvhwdab6he4erk46wy