DGL Implementation of the Node2vec

This DGL example implements the graph embedding model proposed in the paper node2vec: Scalable Feature Learning for Networks

The author's codes of implementation is in Node2vec

Example implementor

This example was implemented by Smile 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:

python main.py --task="train"

To time node2vec random walks:

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.