RpHGNN

Source code and dataset of the paper "Efficient Heterogeneous Graph Learning via Random Projection"

Homepage and Paper

• Homepage (RpHGNN): https://github.com/CrawlScript/RpHGNN
• Paper: Efficient Heterogeneous Graph Learning via Random Projection

Requirements

• Linux
• Python 3.7
• torch==1.12.1+cu113
• torchmetrics==0.11.4
• dgl==1.0.2+cu113
• ogb==1.3.5
• shortuuid==1.0.11
• pandas==1.3.5
• gensim==4.2.0
• numpy==1.21.6
• tqdm==4.64.1

Download Preparation

For HGB datasets (ACM, DBLP, Freebase, and IMDB)：

sh download_hgb_datasets.sh 

For OAG-Venue and OAG-L1-Field, we follow NARS' data prepatation in https://github.com/facebookresearch/NARS/tree/main/oag_dataset. After generating *.pk and *.npy files, you have to:

• put these files in the directory
• rename graph_field.pk to graph_L1.pk

For OGBN-MAG, the code will automatically download it via the ogb package.

For OAG-Venue and OAG-L1-Field, we adhere to NARS' data preparation instructions found at https://github.com/facebookresearch/NARS/tree/main/oag_dataset. After generating *.pk and *.npy files, you should:

• Place these files in the directory ./datasets/nars_academic_oag/.
• Rename graph_field.pk to graph_L1.pk.

Run RpHGNN

You can run RpHGNN with the following command:

sh scripts/run_ACM.sh

sh scripts/run_DBLP.sh

sh scripts/run_Freebase.sh

sh scripts/run_IMDB.sh

sh scripts/run_OGBN-MAG.sh

sh scripts/run_OAG-Venue.sh

sh scripts/run_OAG-L1-Field.sh

Run RpHGNN for OGB Leaderboards (ogbn-mag)

To reproduce the results on the OGB Leaderboards (ogbn-mag), follow the steps below:

• Preparing Pre-trained Embeddings (Optional):

  • If the cache/mag.p file does not exist (embeddings pre-trained via LINE [1]), our code will automatically pre-train it and save the pre-trained embeddings in the specified path.
  • Alternatively, if you'd prefer to skip the pre-training step, download the pre-trained embeddings mag.p directly from Google Drive and place it in the cache directory.

• Execute the script:

  sh scripts/run_leaderboard_OGBN-MAG.sh

  This script will run the training and evaluation using random seeds from 0 to 9. The output for seed i will be saved in the file nohup_leaderboard_mag_i.out.

References:

• [1] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. "Line: Large-scale information network embedding." In Proceedings of the 24th international conference on world wide web, pp. 1067-1077. 2015.

Cite

If you use RpHGNN in a scientific publication, we would appreciate citations to the following paper:

@misc{hu2023efficient,
      title={Efficient Heterogeneous Graph Learning via Random Projection}, 
      author={Jun Hu and Bryan Hooi and Bingsheng He},
      year={2023},
      eprint={2310.14481},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

License: GPLv3

Copyright (c) 2023-2024 Xtra Computing Group, NUS, Singapore.