https://github.com/caojiangxia/BiGI

[WSDM 2021]Bipartite Graph Embedding via Mutual Information Maximization
https://github.com/caojiangxia/BiGI

bipartite-graphs deep-infomax graph-embedding graph-neural-networks recommender-system self-supervised-learning

Last synced: about 2 months ago
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[WSDM 2021]Bipartite Graph Embedding via Mutual Information Maximization

• Host: GitHub
• URL: https://github.com/caojiangxia/BiGI
• Owner: caojiangxia
• License: mit
• Created: 2020-10-19T08:16:52.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-07-06T09:49:17.000Z (about 3 years ago)
• Last Synced: 2024-06-17T18:02:30.113Z (3 months ago)
• Topics: bipartite-graphs, deep-infomax, graph-embedding, graph-neural-networks, recommender-system, self-supervised-learning
• Language: Python
• Homepage: https://arxiv.org/abs/2012.05442
• Size: 1.89 MB
• Stars: 73
• Watchers: 5
• Forks: 13
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
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](https://grouplens.org/datasets/movielens/).

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:

```shell

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:

```shell

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:

```shell

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):

```shell

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):

```shell

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&

```