Image credit: https://tkipf.github.io/graph-convolutional-networks/
Graph Neural Networks (GNNs) have gained immense popularity as a new methodology of analysing graphs, similar to how we use deep learning to process images. GNNs can process many real‐world datasets, which inherently have graph structures. They also provide application scenarios in different domains, from recommendation systems to road traffic predictions and drug design. GNNs use the same principles as Convolutional Neural Networks (CNNs) with re‐definition for the graph domain. Like in images, the convolution framework of GNNs is capturing neighbourhood information of graph nodes to process graph structures represent in molecules, social‐, citation‐ and road networks. With the advancements of machine learning, graph data processing is nowadays possible in the most effective manner.
This repository contains the training and inference source code of a GCN for our technology platform. Please note that model training requires TensorFlow 1.15 and model inference our pyACL framework.
Please use the following link to visit our repository. http://shrnk.cc/sdrwl