wehub-resource-sync
66 lines
1.7 KiB
Markdown
66 lines
1.7 KiB
Markdown
Inductive Representation Learning on Large Graphs (GraphSAGE)
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============
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- Paper link: [http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf](http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf)
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- Author's code repo: [https://github.com/williamleif/graphsage-simple](https://github.com/williamleif/graphsage-simple)
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For advanced usages, including training with multi-gpu/multi-node, and PyTorch Lightning, etc., more examples can be found in [advanced](https://github.com/dmlc/dgl/tree/master/examples/pytorch/graphsage/advanced) and [dist](https://github.com/dmlc/dgl/tree/master/examples/pytorch/graphsage/dist) directory.
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Requirements
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------------
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```bash
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pip install requests torchmetrics==0.11.4 ogb
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```
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How to run
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-------
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### Full graph training
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Run with following (available dataset: "cora", "citeseer", "pubmed")
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```bash
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python3 train_full.py --dataset cora --gpu 0 # full graph
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```
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Results:
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```
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* cora: ~0.8330
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* citeseer: ~0.7110
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* pubmed: ~0.7830
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```
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### Minibatch training for node classification
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Train w/ mini-batch sampling in mixed mode (CPU+GPU) for node classification on "ogbn-products"
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```bash
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python3 node_classification.py
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```
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Results:
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```
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Test Accuracy: 0.7632
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```
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### PyTorch Lightning for node classification
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Train w/ mini-batch sampling for node classification with PyTorch Lightning on OGB-products. It requires PyTorch Lightning 2.0.1. It works with both single GPU and multiple GPUs:
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```bash
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python3 lightning/node_classification.py
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```
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### Minibatch training for link prediction
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Train w/ mini-batch sampling for link prediction on OGB-citation2:
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```bash
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python3 link_pred.py
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```
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Results (10 epochs):
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```
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Test MRR: 0.7386
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```
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