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[NeurIPS'24] The source code for "Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning".

InfoMGF 최신버젼 다운로드

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# Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning

This repository contains the source code and datasets for the NeurIPS'24 paper "Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning".

Paper Link: [https://openreview.net/pdf?id=xaqPAkJnAS](https://openreview.net/pdf?id=xaqPAkJnAS)

NeurIPS Project Link (including poster and slides): [https://neurips.cc/virtual/2024/poster/93075](https://neurips.cc/virtual/2024/poster/93075)

The overall framework:

![InfoMGF Fig](https://github.com/zxlearningdeep/InfoMGF/blob/main/framework.png)

# Available Data

All the datasets can be downloaded from [datasets link](https://drive.google.com/file/d/1WU8j5YbwNr-cD-UQEfqX8nRDW0maDbWR/view?usp=sharing).

**Place the 'data' folder from the downloaded files into the 'InfoMGF' directory.**

The adjacency matrices of the multiplex graph are extracted from publicly available heterogeneous graph datasets using different meta-paths. By utilizing meta-paths, we can transform the heterogeneous graph into homogeneous graphs with multiplex structures. You can take a look at our code "data_process.py" for the detailed dataset preprocessing process. If you want to learn more about heterogeneous graphs and meta-paths, we recommend the following papers:

1. Wang, Xiao, et al. “Heterogeneous graph attention network.” The World Wide Web Conference. 2019.
2. Shen, Zhixiang, and Zhao Kang. “When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation Learning.” [arXiv preprint arXiv:2409.00687 (2024)](https://arxiv.org/abs/2409.00687).

# Requirements

This code requires the following:

* Python==3.9.16
* PyTorch==1.13.1
* DGL(cuda)==0.9.1
* Numpy==1.24.2
* Scipy==1.10.1
* Scikit-learn==1.2.1
* Munkres==1.1.4
* kmeans-pytorch==0.3

# Training

`python main.py -dataset acm`

Here, "acm" can be replaced by "dblp", "yelp","mag".

# BibTeX

```

@inproceedings{shen2024beyond,
 author = {Shen, Zhixiang and Wang, Shuo and Kang, Zhao},
 booktitle = {Advances in Neural Information Processing Systems},
 pages = {31629--31658},
 title = {Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning},
 volume = {37},
 year = {2024}
}

```

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