The official PyTorch implementation of DCRS in the following paper:
Jiazheng Zhang, Bang Wang. 2023. Encoding Node Diffusion Competence and Role Significance for Network Dismantling.
In WWW'23, April 30 โ May 4, 2023, Austin, Texas, USA, 12 pages.
- torch 1.7.1
- igraph 0.9.9
- networkx 2.6.3
- scipy 1.5.4
- sklearn 0.24.2
- numpy 1.19.5
- dgl 0.7.0
Install all dependencies using
pip install -r requirement.txt
- Unzip the
data.zip
and put them in the folder.\data
. - Train and evaluate the model on real-world networks :
python train_realworld.py
- Train and evaluate the model on synthetic networks :
python train_synthetic.py --Type BA --N 1000 --M 4
--Type
argument should be one of [ BA, WS, PLC, ER], --N
set the size of synthetic networks, --M
set the synthetic generation parameter.
Please cite our work if you find our code/paper is helpful to your work.
@inproceedings{zhang2023DCRS,
title={Encoding Node Diffusion Competence and Role Significance for Network Dismantling},
author={Zhang, Jiazheng and Wang, Bang},
booktitle={Proceedings of the 2023 ACM Web Conference},
series={WWW'23},
year={2023},
location={Austin, Texas, USA},
numpages={12}
}