An **RGCN**, or **Relational Graph Convolution Network**, is a an application of the [GCN framework](https://paperswithcode.com/method/gcn) to modeling relational data, specifically
to link prediction and entity classification tasks.

See [here](https://docs.dgl.ai/en/0.4.x/tutorials/models/1_gnn/4_rgcn.html) for an in-depth explanation of RGCNs by DGL.

**Stable Rank Normalization (SRN)** is a weight-normalization scheme which minimizes the
stable rank of a linear operator. It simultaneously controls the Lipschitz constant and the stable rank of a linear operator. Stable rank is a softer version of the rank operator and is defined as the squared ratio of the Frobenius norm to the spectral norm.

Stable Rank Normalization for Improved Generalization in Neural Networks and GANs

RGCN

Modeling Relational Data with Graph Convolutional Networks

**ProGAN**, or **Progressively Growing GAN**, is a generative adversarial network that utilises a progressively growing training approach. The idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses.

Source	Modeling Relational Data with Graph Convolutional Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com