chore: import upstream snapshot with attribution

2026-07-13 13:35:51 +08:00
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+# DGL Implementation of the Node2vec
+This DGL example implements the graph embedding model proposed in the paper 
+[node2vec: Scalable Feature Learning for Networks](https://arxiv.org/abs/1607.00653) 
+
+The author's codes of implementation is in [Node2vec](https://github.com/aditya-grover/node2vec) 
+
+
+Example implementor
+----------------------
+This example was implemented by [Smile](https://github.com/Smilexuhc) during his intern work at the AWS Shanghai AI Lab.
+
+The graph dataset used in this example 
+---------------------------------------
+
+cora
+ - NumNodes: 2708
+ - NumEdges: 10556
+
+ogbn-products
+ - NumNodes: 2449029
+ - NumEdges: 61859140
+
+ 
+Dependencies
+--------------------------------
+
+- python 3.6+
+- Pytorch 1.5.0+
+- ogb  
+
+
+ How to run example files
+--------------------------------
+To train a node2vec model:
+```shell script
+python main.py --task="train"
+```
+
+To time node2vec random walks:
+```shell script
+python main.py --task="time" --runs=10
+```
+
+Performance
+-------------------------
+
+**Setting:** `walk_length=50, p=0.25, q=4.0`
+| Dataset  |     DGL     |     PyG     |
+| -------- | :---------: | :---------: |
+| cora     | 0.0092s | 0.0179s |
+| products | 66.22s  | 77.65s  |
+Note that the number in table are the average results of multiple trials.  
+For cora, we run 50 trials.  For ogbn-products, we run 10 trials.