Awesome

BiGI

The source code is for the paper: ”Bipartite Graph Embedding via Mutual Information Maximization" accepted in WSDM 2021 by Jiangxia Cao*, Xixun Lin*, Shu Guo, Luchen Liu, Tingwen Liu, Bin Wang (* means equal contribution).

@inproceedings{bigi2021,
  title={Bipartite Graph Embedding via Mutual Information Maximization},
  author={Cao*, Jiangxia and Lin*, Xixun and Guo, Shu and Liu, Luchen and Liu, Tingwen and Wang, Bin},
  booktitle={ACM International Conference on Web Search and Data Mining (WSDM)},
  year={2021}
}

Requirements

Python=3.6.2

PyTorch=1.1.0

CUDA=9.0

Scikit-Learn = 0.22

Scipy = 1.3.1

Preparation

Some datasets have been included in the ./dataset directory. Other datasets can be downloaded from the official website.

Usage

To run this project, please make sure that you have the following packages being downloaded. Our experiments are conducted on a PC with an Intel Xeon E5 2.1GHz CPU and a Tesla V100 GPU.

For running DBLP:

CUDA_VISIBLE_DEVICES=1 nohup python -u train_rec.py --id dblp --struct_rate 0.00001 --GNN 2 > BiGIdblp.log 2>&1&

For running ML-100K:

CUDA_VISIBLE_DEVICES=1 nohup python -u train_rec.py --data_dir dataset/movie/ml-100k/1/ --batch_size 128 --id ml100k --struct_rate 0.0001 --GNN 2 > BiGI100k.log 2>&1&

For running ML-10M:

CUDA_VISIBLE_DEVICES=1 nohup python -u train_rec.py --batch_size 100000 --data_dir dataset/movie/ml-10m/ml-10M100K/1/ --id ml10m --struct_rate 0.00001 > BiGI10m.log 2>&1&

For running Wiki(5:5):

CUDA_VISIBLE_DEVICES=1 nohup python -u train_lp.py --id wiki5 --struct_rate 0.0001 --GNN 2 > BiGIwiki5.log 2>&1&

For running Wiki(4:6):

CUDA_VISIBLE_DEVICES=1 nohup python -u train_lp.py --data_dir dataset/wiki/4/ --id wiki4 --struct_rate 0.0001 --GNN 2 > BiGIwiki4.log 2>&1&