GATNE-T

Representation Learning for Attributed Multiplex Heterogeneous Network (GANTE)

• Paper link: https://arxiv.org/abs/1905.01669
• Author's code repo: https://github.com/THUDM/GATNE. Note that only GATNE-T is implemented here.

Requirements

• requirements

pip install -r requirements.txt

Datasets

To prepare the datasets:

1. mkdir data
   cd data

2. Download datasets from the following links:
   • example: https://s3.us-west-2.amazonaws.com/dgl-data/dataset/recsys/GATNE/example.zip
   • amazon: https://s3.us-west-2.amazonaws.com/dgl-data/dataset/recsys/GATNE/amazon.zip
   • youtube: https://s3.us-west-2.amazonaws.com/dgl-data/dataset/recsys/GATNE/youtube.zip
   • twitter: https://s3.us-west-2.amazonaws.com/dgl-data/dataset/recsys/GATNE/twitter.zip
3. Unzip the datasets

Training

Run with following (available dataset: "example", "youtube", "amazon")

python src/main.py --input data/example

To run on "twitter" dataset, use

python src/main.py --input data/twitter --eval-type 1 --gpu 0

For a big dataset, use sparse to avoid cuda out of memory in backward

python src/main_sparse.py --input data/example --gpu 0

If you have multiple GPUs, you can also accelerate training with DistributedDataParallel

python src/main_sparse_multi_gpus.py --input data/example --gpu 0,1

It is worth noting that DistributedDataParallel will cause more cuda memory consumption and a certain loss of preformance.

Results

All the results match the official code with the same hyper parameter values, including twiiter dataset (auc, pr, f1 is 76.29, 76.17, 69.34, respectively).

	auc	pr	f1
amazon	96.88	96.31	92.12
youtube	82.29	80.35	74.63
twitter	72.40	74.40	65.89
example	94.65	94.57	89.99