Variational Autoencoders is a type of GAE method that combine dimension reduction methods and generative models. For example, Deep Variational Network Embedding (DVNE) represents each node as a Gaussian Distribution and does dimension reduction by using the Wasserstein distance to promote nodes similarities.

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Autoencoder (AE) is a type of neural network that aims to learn encodings of unlabeled data, and it’s evaluated by mapping the generated encoding back to the original input (decoder). It can be used to learn graph representation with moderate modification. Below are several types of it:

1)	Original Graph Autoencoders

2)	Graph Variational Autoencoders

3)	Other Improvements.


Graph Autoencoders (GAE)

Zhang, Z., Cui, P., & Zhu, W. (2020). Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering.

Deep Learning on Graphs: A Survey

This type of GAE is the most basic type of GAE. It views the adjacency matrix as features of nodes, and uses AE to reduce its dimension and obtain new representation. For example, Structure deep network embedding (SDNE) aims to let two nodes share similar representations if they have similar neighborhoods, and both first and second order nodes are preserved.

Original Graph Autoencoders

Variational Autoencoders (VAE)

There are several perspective to improve a GAE model:

1)	Adversarial Training

2)	Inductive Learning

3)	Similarity Measures


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