dmlc--dgl
Quan (Andy) Gan
0d878ff8ef
* cleaned pl node classification example * conform to PL's method of updating the dataloader * update * lint * fix test * fix
56 行
1.5 KiB
Markdown
56 行
1.5 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). Note that the original code is
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simple reference implementation of GraphSAGE.
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Advanced usages, including how to run pure GPU sampling, how to train with PyTorch Lightning, etc., are in the `advanced` directory.
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Requirements
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------------
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```bash
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pip install requests torchmetrics
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```
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Results
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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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* cora: ~0.8330
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* citeseer: ~0.7110
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* pubmed: ~0.7830
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### Minibatch training for node classification
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Train w/ mini-batch sampling for node classification on OGB-products:
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```bash
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python3 node_classification.py
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python3 multi_gpu_node_classification.py
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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.
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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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