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7c7113f · May 17, 2021

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GATNE-T

Representation Learning for Attributed Multiplex Heterogeneous Network (GANTE)

Requirements

  • requirements
pip install -r requirements.txt

Datasets

To prepare the datasets:

  1. mkdir data
    cd data
  2. Download datasets from the following links:
  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