Node Classification using Graph Convolutional Neural Network

Node Classification on Cora Dataset in PyTorch using GCN

Prerequisites:

Graph Basics and Application of Graph

Graph Representational Learning

Graph Neural Networks: A Deep Neural Network for Graphs

Dataset: This article uses Cora Dataset, consisting of 2708 scientific publications classified into one of seven different classes. The citation network consists of 5429 links.

Objective: Node classification using GCN to accurately predict the subject of a paper given its words and citation network using PyTorch geometric

Graph Convolutional Neural Network(GCN) model is a framework of spectral graph convolutions applying a generalization of convolutions to non-Euclidean data.

GCNs are similar to convolutions applied to images as they generalize the graph data's convolution operations. The filter parameters are shared over all locations in the graph. GCN is based on graph convolutions built by stacking multiple convolutional layers, and a point-wise non-linearity function follows each layer.

source: http://tkipf.github.io/graph-convolutional-networks/

In this example, you will classify the scientific papers in a citation graph where labels are only available for a small…