… For example, in social networks, users need to be divided into meaningful social groups
to … In Sections 3.2, 3.3 and 3.4, we will introduce the variational graph auto-encoder, joint …

… To overcome this limitation, the Variational Auto-Encoder (VAE) was developed by
incorporating a priori constraints into the embedding learning process. Rather than learning the …

… of the information networks in our daily life have rich attribute … networks, or users’ education
background in social networks. … a network-specific variational autoencoder for embedding …

… Based on the variational auto-encoder, our proposed method achieves better results on
recall and … Network-specific variational auto-encoder for embedding in attribute networks. In: …

… We notice that most graph auto-encoder models overlook the latent ties or simply approximate
… Zhang, “Network-specific variational auto-encoder for embedding in attribute networks,” in …

… to compare nodes and attributes. In this paper, we utilize variational autoencoder to address
these shortcomings. … Zhang, “Network-specific variational auto-encoder for embedding in …

… topology and attribute in unsupervised scenarios. In this paper, we propose an end-to-end
network embedding method. By employing the high order graph convolutional networks, our …

… and affect community structures of networks. Thus, many NE approaches (… a network-specific
VAE (NetVAE) method for learning network topology and attribute information embedding. …

… While auto-encoder (AE) based graph embedding (GE) … often limited to networks composed
of nodes with attributes and … of resource-centric network-specific graphs within the deep …

… Network-specific variational auto-encoder for embedding in attribute networks. In Proceedings
of … Graph convolutional networks meet markov random fields: Semi-supervised community …