To understand what an autoencoder has learned through its unsupervised learning process. To rank hidden nodes according to their capability of performing a learning task. To identify the specialty ...

Abstract: The purpose of graph embedding is to encode the known node features ... with a large iterative learning time, and susceptible to local optimal solutions. Thus, we propose Graph Convolutional ...

This is a Keras implementation of the symmetrical autoencoder architecture with parameter sharing for the tasks of link prediction and semi-supervised node classification, as described in the ...

Abstract: Traditional machine-learning ... method based on a Graph autoencoder embedded AutoEncoder, named GeAE, is introduced to learn invariant representations across domains. The proposed approach ...