DBLP paper classification

Prerequisites

• Linux
• Python 3.9
• PyTorch 1.10.0+cu111

Getting Started

Installation

Clone this repo.

git clone https://github.com/QingFei1/dpv12_nodeclassification.git
cd dpv12_nodeclassification

Please install dependencies by

pip install -r requirements.txt

Dataset

The DBLP paper dataset can be downloaded from AMiner and the venue-to-label data can be downloaded from Baidu Pan (with password ihj6) or Aliyun. Put downloaded files into the data directory of the project directory and unzip DBLP papers.

How to run

Processing: run cell by cell in data_filter.ipynb

python x_oag_bert.py  # generate node embeddings
python test_dpv12.py  # uncomment the corresponding lines to run sgc, sign, and graphsage.

Results

Variant	test_acc	val_acc
sgc	0.3408±0.0150	0.3144±0.0117
sign	0.2625±0.0001	0.2499±0.0000
graphsage	0.5957±0.0224	0.5712±0.0237

References

🌟 If you find our work helpful, please leave us a star and cite our paper.

@inproceedings{zhang2024oag,
  title={OAG-bench: a human-curated benchmark for academic graph mining},
  author={Fanjin Zhang and Shijie Shi and Yifan Zhu and Bo Chen and Yukuo Cen and Jifan Yu and Yelin Chen and Lulu Wang and Qingfei Zhao and Yuqing Cheng and Tianyi Han and Yuwei An and Dan Zhang and Weng Lam Tam and Kun Cao and Yunhe Pang and Xinyu Guan and Huihui Yuan and Jian Song and Xiaoyan Li and Yuxiao Dong and Jie Tang},
  booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={6214--6225},
  year={2024}
}