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.

**Invertible Image Conversion Net**, or **IICNet**, is a generic framework for reversible image conversion tasks. Unlike previous encoder-decoder based methods, IICNet maintains a highly invertible structure based on invertible neural networks (INNs) to better preserve the information during conversion. It uses a relation module and a channel squeeze layer to improve the INN nonlinearity to extract cross-image relations and the network flexibility, respectively.

IICNet

IICNet: A Generic Framework for Reversible Image Conversion

RGCN

Modeling Relational Data with Graph Convolutional Networks

MinCutPool is a trainable pooling operator for graphs that learns to map nodes into clusters.
The method is trained to approximate the minimum K-cut of the graph to ensure that the clusters are balanced, while also jointly optimizing the objective of the task at hand.

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