dmlc--dgl/examples/graphbolt/pyg

Overview

This project demonstrates the training and evaluation of a GraphSAGE model for node classification on large graphs. The example utilizes GraphBolt for efficient data handling and PyG for the GNN training.

Node classification on graph

This example aims to demonstrate how to run node classification task on heterogeneous graph with GraphBolt.

Model

The model is a three-layer GraphSAGE network implemented using PyTorch Geometric's SAGEConv layers.

Default Run on ogbn-arxiv dataset

python node_classification.py

Accuracies

Final performance(for ogbn-arxiv): 
All runs:
Highest Train: 62.26
Highest Valid: 59.89
Final Train: 62.26
Final Test: 52.78

Run on ogbn-products dataset

Sample on CPU and train/infer on CPU

python node_classification.py --dataset ogbn-products

Accuracies

Final performance(for ogbn-products): 
All runs:
Highest Train: 90.79
Highest Valid: 89.86
Final Train: 90.79
Final Test: 75.24