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. Here, data is assumed to have some prior distribution, and the GAE is used as the generator, so the GAE is forced to match the prior.

University of Notre Dame

There are several perspective to improve a GAE model:

1)	Adversarial Training

2)	Inductive Learning

3)	Similarity Measures


Improvements of 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

Improving GAE using Adversarial Training

Similar to GCN, some GAE methods incorporate nodes attributes into encoder and edge information in the training set is only used when training, so that the mapping function can be applied to nodes or graphs that are not seen in the training set.

Improving GAE using Inductive Learning

There are lots of similarity measures, such as L2-reconstruction loss, Laplacian eigenmaps, and KL divergence. There are different motivations for using them, but the study of their underlying difference still lacking.

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