{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Relational Graph Convolutional Network\n\n**Author:** Lingfan Yu, Mufei Li, Zheng Zhang\n\n
The tutorial aims at gaining insights into the paper, with code as a mean\n of explanation. The implementation thus is NOT optimized for running\n efficiency. For recommended implementation, please refer to the [official\n examples](https://github.com/dmlc/dgl/tree/master/examples).
Another weight regularization, block-decomposition, is implemented in\n the [link prediction](link-prediction_).
Each relation type is associated with a different weight. Therefore,\n the full weight matrix has three dimensions: relation, input_feature,\n output_feature.
This is showing how to implement an R-GCN from scratch. DGL provides a more\n efficient :class:`builtin R-GCN layer module