node2vec: Scalable Feature Learning for Networks

Node2vec is an algorithmic framework for representational learning on graphs. Given any graph, it can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks. Based on PGL, we reproduce node2vec algorithms and reach the same level of indicators as the paper.

Datasets

The datasets contain two networks: BlogCatalog and Arxiv.

Dependencies

• paddlepaddle>=1.4

• pgl

How to run

For examples, use gpu to train gcn on cora dataset.

# multiclass task example
python node2vec.py --use_cuda --dataset BlogCatalog --save_path ./tmp/node2vec_BlogCatalog/ --offline_learning --epoch 400

python multi_class.py --use_cuda --ckpt_path ./tmp/node2vec_BlogCatalog/paddle_model --epoch 1000

# link prediction task example
python node2vec.py --use_cuda --dataset ArXiv --save_path
./tmp/node2vec_ArXiv --offline_learning --epoch 10

python link_predict.py --use_cuda --ckpt_path ./tmp/node2vec_ArXiv/paddle_model --epoch 400

Hyperparameters

• dataset: The citation dataset “BlogCatalog” and “ArXiv”.

• use_cuda: Use gpu if assign use_cuda.

Experiment results

Dataset	model	Task	Metric	PGL Result	Reported Result
BlogCatalog	deepwalk	multi-label classification	MacroF1	0.250	0.211
BlogCatalog	node2vec	multi-label classification	MacroF1	0.262	0.258
ArXiv	deepwalk	link prediction	AUC	0.9538	0.9340
ArXiv	node2vec	link prediction	AUC	0.9541	0.9366