In adversarial training, there is one generator and one discriminator. The generator aims to generate fake data and “fool” the discriminator into thinking it’s real data, and the discriminator aims to tell fake data from real data. So both model benefit from the improvement of each other.

Adversarial Training

Adversarial Attack is a type of adversarial method that “fools” the discriminator by adding small perturbation to the original data. For example, Nettack modifies graph structures and nodes attributes, and uses a loss function that aims to find the best legitimate change to cause a node to be misclassified.

Adversarial Attacks

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


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)

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.                                     

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