README.md

GCN (Graph Convolutional Network)

Introduction

Graph convolutional network is known as one of the most prominent progress for deep learning based method on graph structure data. Convolution operator on GCN is a localized first-order approximation of spectral graph convolution. In spatial perspective, embeddings of neighbor nodes are aggregated together to update node's self embedding.

Key points to build a GCN

• sample EgoGraphs
• encode EgoGraphs using GCN convolutional layers.

Original GCN uses full graph as input, for efficient large-scale training, we implemente a sample based version of GCN. For sample based GCN, we use dense format (because sampled number of neighbor nodes are fixed, so they can form a dense tensor) of EgoGraph and for original GCN, we use a sparse format of EgoGraph (we use sparse tensor to deal with unaligned neighbor numbers) is used for batch training.

How to run

1. Prepare data

   cd ../../data/
   python cora.py

2. Train and evalute

   python train_supervised.py

Datasets and performance

Dataset	ACC
Cora	~0.80

Reference paper

semi-supervised classification with graph convolutional networks