ki-ljl / POFD

Pytorch Code for NeurIPS 2023 Paper---Public Opinion Field Effect Fusion in Representation Learning for Trending Topics Diffusion.

POFD

Code for NeurIPS 2023 paper---Public Opinion Field Effect Fusion in Representation Learning for Trending Topics Diffusion.

Overview

POFD:.
│  get_data.py
│  pytorchtools.py
│  requirements.txt
│          
├─data
│  ├─BuzzFeed
│  │      
│  ├─DBLP
│  │      
│  └─PolitiFact
│          
└─src
    │  infomax.py
    │  lp_main.py
    │  models.py
    │  nc_dblp_main.py
    │  nc_main.py
    │  util.py
    │  
    └─checkpoints   

1. get_data.py: This file is used to process the data.
2. pytorchtools.py: This file is used to define the early_stopping mechanism.
3. requirements.txt: Dependencies file.
4. data/：Dataset folder.
5. src/infomax.py: This file is used to maximize the information, i.e., to calculate $L_p$.
6. src/lp_main.py: Public opinion concern prediction (Section 4.2).
7. src/models.py: POFD implementation.
8. src/nc_dblp_main.py: Universality analysis (Section 4.4).
9. src/nc_main.py: Event classification (Section 4.3).
10. src/util.py: Defining various toolkits.

Dependencies

Please install the following packages:

gensim==3.8.3
huggingface-hub==0.12.1
joblib==1.2.0
matplotlib==3.6.3
networkx==2.8.8
node2vec==0.3.3
numpy==1.22.4
pandas==1.3.3
scikit-learn==1.2.1
scipy==1.8.0
torch==1.12.1+cu113
torch-cluster==1.6.0+pt112cu113
torch-geometric==2.2.0
torch-scatter==2.1.0+pt112cu113
torch-sparse==0.6.16+pt112cu113
torch-spline-conv==1.2.1+pt112cu113
tqdm==4.62.3
transformers==4.26.1

You can also simply run:

pip install -r requirements.txt

Public Opinion Concern Prediction

cd src/
python lp_main.py --dataset BuzzFeed
python lp_main.py --dataset PolitiFact

Event Classification

cd src/
python nc_main.py --dataset BuzzFeed
python nc_main.py --dataset PolitiFact

Universality Analysis

cd src/
python nc_dblp_main.py

cite

@article{li2024public,
  title={Public Opinion Field Effect Fusion in Representation Learning for Trending Topics Diffusion},
  author={Li, Junliang and Yajun, Yang and Hu, Qinghua and Wang, Xin and Gao, Hong},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}