hybridpool4mixup
[JSS 2024] On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing
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[JSS 2024] On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing
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- Stars: 2
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Metadata Files
README.md
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing
Implementation of Journal of Systems and Software (JSS 2024) paper: On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing [arxiv].
We build this project on the top of GNN-FakeNews and Project_CodeNet. Please refer to these projects for more details.
Introduction
The performance of linear interpolation methods, especially those used for graph learning, can be significantly influenced by graph pooling operators. To investigate this, we conduct a comprehensive empirical study by applying Manifold-Mixup to a formal characterization of graph pooling. This study encompasses 11 graph pooling operations, including 9 hybrid and 2 non-hybrid pooling operators, to explore how these operators affect the performance of Mixup-based graph learning.
Requirements
On Ubuntu:
Installation
install python packages
shell pip install tqdm pip install pandas pip install ogb pip install keras pip install scikit-learn pip install scipyTask: Problem Classification
shell Python>=3.6 cuDNN>=7.6 PyTorch>=version 1.8.0) Pytorch Geometric>=version 1.6.3 CUDA 11.0Task: Fake News Detection
shell Python>=3.6 cuDNN>=7.6 Pytorch>=version 1.8.0 Pytorch Geometric>=version 1.6.3 CUDA 11.0Experiments
The script to run the experiments is
./run.shTask: Problem Classification ```shell cd text_detection
./run.sh
- Task: Fake News Detection
shell
cd Problem_classification/gnn-based-experiments
./run.sh ```
Dataset
- Java250: https://developer.ibm.com/exchanges/data/all/project-codenet/
- Python800: https://developer.ibm.com/exchanges/data/all/project-codenet/
- Gossipcop/Politifact: https://drive.google.com/drive/folders/1OslTX91kLEYIi2WBnwuFtXsVz5SS_XeR?usp=sharing
Citation
If you use the code in your research, please cite:
bibtex
@article{DONG2024112139,
title = {On the effectiveness of hybrid pooling in mixup-based graph learning for language processing},
journal = {Journal of Systems and Software},
volume = {216},
pages = {112139},
year = {2024},
issn = {0164-1212},
doi = {https://doi.org/10.1016/j.jss.2024.112139},
author = {Zeming Dong and Qiang Hu and Zhenya Zhang and Yuejun Guo and Maxime Cordy and Mike Papadakis and Yves Le Traon and Jianjun Zhao},
}
Owner
- Name: DONG
- Login: zemingd
- Kind: user
- Location: Fukuoka，Japan
- Company: Kyushu University
- Repositories: 1
- Profile: https://github.com/zemingd
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Dong" given-names: "Zeming" orcid: "https://orcid.org/0009-0007-7742-0264" title: "HybridPool4Mixup" version: 1.0.0 doi: 10.5281/zenodo.1234 date-released: 2024-06-14 url: "https://github.com/zemingd/HybridPool4Mixup"