Convolution operations can be divided into two types:

1)	Spectral convolution

2)	Spatial convolution

3)	Mixture of spectral convolution and special convolution


Types of Convolution Operation

1)	Attention Mechanism

2)	Residual and Jumping Connections

3)	Utilizing Edge Features

4)	Sampling Methods

5)	Inductive Setting

6)	Theoretical Analysis


Possible Improvements of GCN

When a graph-level representation is needed to tackle graph-level tasks, node-level representations can be aggregated into a single graph-level representation. This aggregation process is referred to as a readout operation.

Graph Readout Operation

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

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)

Graph Convolutional Networks (GCN)

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)

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


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