wehub-resource-sync
54 lines
1.3 KiB
Markdown
54 lines
1.3 KiB
Markdown
# DGL Implementation of the Node2vec
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This DGL example implements the graph embedding model proposed in the paper
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[node2vec: Scalable Feature Learning for Networks](https://arxiv.org/abs/1607.00653)
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The author's codes of implementation is in [Node2vec](https://github.com/aditya-grover/node2vec)
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Example implementor
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----------------------
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This example was implemented by [Smile](https://github.com/Smilexuhc) during his intern work at the AWS Shanghai AI Lab.
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The graph dataset used in this example
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---------------------------------------
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cora
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- NumNodes: 2708
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- NumEdges: 10556
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ogbn-products
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- NumNodes: 2449029
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- NumEdges: 61859140
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Dependencies
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--------------------------------
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- python 3.6+
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- Pytorch 1.5.0+
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- ogb
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How to run example files
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--------------------------------
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To train a node2vec model:
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```shell script
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python main.py --task="train"
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```
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To time node2vec random walks:
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```shell script
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python main.py --task="time" --runs=10
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```
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Performance
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-------------------------
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**Setting:** `walk_length=50, p=0.25, q=4.0`
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| Dataset | DGL | PyG |
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| -------- | :---------: | :---------: |
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| cora | 0.0092s | 0.0179s |
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| products | 66.22s | 77.65s |
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Note that the number in table are the average results of multiple trials.
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For cora, we run 50 trials. For ogbn-products, we run 10 trials.
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