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}
}