dmlc--dgl
Minjie Wang
36 行
849 B
Markdown
36 行
849 B
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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Requirements
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------------
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- requests
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``bash
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pip install requests
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``
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Results
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-------
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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
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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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Train w/ mini-batch sampling (on the Reddit dataset)
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```bash
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python3 train_sampling.py
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```
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Accuracy: 0.9504
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