wehub-resource-sync
58 lines
1007 B
Markdown
58 lines
1007 B
Markdown
## Overview
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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.
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# Node classification on graph
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This example aims to demonstrate how to run node classification task on heterogeneous graph with **GraphBolt**.
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## Model
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The model is a three-layer GraphSAGE network implemented using PyTorch Geometric's SAGEConv layers.
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## Default Run on `ogbn-arxiv` dataset
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```
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python node_classification.py
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```
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## Accuracies
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```
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Final performance(for ogbn-arxiv):
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All runs:
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Highest Train: 62.26
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Highest Valid: 59.89
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Final Train: 62.26
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Final Test: 52.78
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```
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## Run on `ogbn-products` dataset
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### Sample on CPU and train/infer on CPU
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```
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python node_classification.py --dataset ogbn-products
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```
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## Accuracies
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```
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Final performance(for ogbn-products):
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All runs:
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Highest Train: 90.79
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Highest Valid: 89.86
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Final Train: 90.79
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Final Test: 75.24
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```
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