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 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.

Variational Autoencoders (VAE)

There are several perspective to improve a GAE model:

1)	Adversarial Training

2)	Inductive Learning

3)	Similarity Measures


Improvements of GAE

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.


University of Notre Dame

According to model structure and training strategies, deep learning algorithms on graphs can be divided into 5 categories:

1)	Graph Recurrent Neural Networks

2)	Graph Convolutional Networks

3)	Graph Autoencoders

4)	Graph Reinforcement Learning

5)	Graph Adversarial Methods


Types of Deep Learning Algorithms on Graphs

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

RNN is a class of neural network models that model sequential data. Examples include Gated Recurrent Units (GRU) or Long Short-Term Memory (LSTM) model. RNN can be adapted to graph modeling with some moderate modification.

Graph Recurrent Neural Networks (RNN)

GCN uses convolution operation and readout operation to grasp node level and graph level patterns.

Graph Convolutional Networks (GCN)

Graph Autoencoders (GAE)

Reinforcement learning is a deep learning method that learns from feedback and is effective in learning interactive games such as hide and seek. It can also be applied in graph setting. For example, Graph Convolutional Policy Network (GCPN) models graphs with a generative model, and regards this model as RL agent, treats link prediction as agent action, using domain-specific rewards, and uses GCN to learn node representation.                                     

Graph Reinforcement Learning (RL)

Adversarial Methods are a promising field and have drawn lots of attention. And there are mainly two ways of applying adversarial methods to graphs:

1) Adversarial Training

2) Adversarial Attacks


Learn Before

Related

Learn After