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This is the implementation of paper "Variational Graph Auto-Encoders", which is published in NIPS 2016 Workshop. Thomas N. Kipf, Max Welling, Variational Graph Auto-Encoders, In NIPS Workshop on ...
Graph Auto-Encoders (GAEs) are end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs. GAEs have successfully been used for: Link prediction in ...
Auto-encoders have emerged as a successful framework for unsupervised learning. However, conventional auto-encoders are incapable of utilizing explicit relations in structured data. To take advantage ...
Our proposed model uses a variant of the graph attention network (GAT) as the encoder and uses variants of ConvE [Conv-TransE (Shang et al., 2019), Conv-TransR] as decoder, to achieve the simultaneous ...
Graph based clustering plays an important role in clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. However, in traditional ...