metapath2vec: Scalable Representation Learning for Heterogeneous Networks¶

metapath2vec is a algorithm framework for representation learning in heterogeneous networks which contains multiple types of nodes and links. Given a heterogeneous graph, metapath2vec algorithm first generates meta-path-based random walks and then use skipgram model to train a language model. Based on PGL, we reproduce metapath2vec algorithm.

Datasets¶

You can dowload datasets from here

We use the “aminer” data for example. After downloading the aminer data, put them, let’s say, in ./data/net_aminer/ . We also need to put “label/” directory in ./data/.

Dependencies¶

paddlepaddle>=1.6
pgl>=1.0.0

Hyperparameters¶

All the hyper parameters are saved in config.yaml file. So before training, you can open the config.yaml to modify the hyper parameters as you like.

for example, you can change the “use_cuda” to “True ” in order to use GPU for training or modify “data_path” to specify the data you want.

Some important hyper parameters in config.yaml:

use_cuda: use GPU to train model
data_path: the directory of dataset that you want to load
lr: learning rate
neg_num: number of negative samples.
num_walks: number of walks started from each node
walk_length: walk length
metapath: meta path scheme

Metapath randomwalk sampling¶

Before training, we should generate some metapath random walks to train skipgram model. we can run the below command to produce metapath randomwalk data.

python sample.py -c config.yaml

Training and Testing¶

After finishing metapath randomwalk sampling, you can run the below command to train and test the model.

python main.py -c config.yaml

python multi_class.py --dataset ./data/out_aminer_CPAPC/author_label.txt --word2id ./checkpoints/train.metapath2vec/word2id.pkl  --ckpt_path ./checkpoints/train.metapath2vec/model_epoch5/

Experiment results¶

train_percent	Metric	PGL Result	Reported Result
50%	macro-F1	0.9249	0.9314
50%	micro-F1	0.9283	0.9365