RobustECD: Enhancement of Network Structure for Robust Community Detection

This is a Python implementation of RobustECD, as described in the following:

RobustECD: Enhancement of Network Structure for Robust Community Detection.

Requirements

The code is tested on Ubuntu 16.04 and Windows 10 with the following components:

Software

Python 3.7
NetworkX 2.4
SciPy 1.4.1
NumPy 1.18.1
python-igraph 0.7.1.post6

Datasets

Real-world benchmark networks:

Karate, Polbooks, Football, Polblogs

Large-scale real-world networks from Stanford Large Network Dataset Collection:

Amazon, DBLP

Adversarial networks generated via adversarial attack on benchmark networks:

Karate_noise, Polbooks_noise, Football_noise, Polblogs_noise

Usage

RobustECD-SE: execute the following bash commands in the same directory where the code resides:
```
$ python exp_revsel.py --bmname karate --cdm LOU --randomSample 1 --sampleRatio 1.6
```
RobustECD-GA: execute the following bash commands in the same directory where the code resides:
```
$ python exp_rega.py --bmname karate --cdm INF --iter 500 -aR 0.16 -dR 0.16
```

Common Parameters:

bmname: name of dataset
- benchmark: karate, polbooks, football, polblogs
- large-scale subgraph: amazon-sub, dblp-sub
- adversarial networks: karate_noise, polbooks_noise, football_noise, polblogs_noise
cdm: community detection method
- Infomap: INF
- Fast Greedy: FG
- WalkTrap: WT
- Louvain: LOU
- Label Propagation: LP
- Node2vec+Kmeans: n2v_km

Citation

If you find this work useful, please cite the following:

@ARTICLE{9454336,
  author={Zhou, Jiajun and Chen, Zhi and Du, Min and Chen, Lihong and Yu, Shanqing and Chen, G. and Xuan, Qi},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={RobustECD: Enhancement of Network Structure for Robust Community Detection}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TKDE.2021.3088844}
}

jjzhou012 / RobustECD