Kipf, T. N., and Welling, M. 2016b. Variational graph autoencoders. NIPS Workshop on Bayesian Deep Learning.

https://arxiv.org/pdf/1611.07308.pdf

University of Michigan - Ann Arbor

The author used the non-probabilistic Graph autoencoder(GAE) and the Variational Graph Autoencoder(VGAE) with 200 epochs, a learning rate of 0.01, two-layers(32 hidden units in the first and 16 in the second). (Kipf and Welling 2016)

What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning: Prerequisite chain learning GCN models

The Graph Autoencoder (GAE) model takes two matrices: an adjacency matrix $$A$$ and a feature matrix $$X$$. The feature matrix $$X$$ has size $$n \times d$$, where $$n$$ is the number of vertices/concepts and $$d$$ is the number of input features per concept.

What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning: GAE algorithm

VGAE is an extension to the GAE model by taking latent variables.

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