The source codes, experimental test-beds and, the datasets of our paper titled "A One-by-One Method for Community Detection in Attributed Networks" by Soroosh Shalileh and, Boris Mirkin
For more information on how to call our algorithm "SEANAC" one can refer to any the demo jupyter notebooks "clustering_results_Lawyers".
Also this algorithm can be run through the terminal by calling:
python SEANAC.py --Name="name of dataset in ../data" --PreProcessing="z-m" --Run=1
Note: data sets should be stored in data directory.
For generating similar synthetic data sets, One should call "synthetic_data_generator.py" as this is demonstrated in Jupyter notebook "MediumSize_synthetic_data.ipynb".
If you use our code, please cite our following paper:
BibTex:
@inproceedings{SEANAC_IDEAL,
title={A One-by-One Method for Community Detection in Attributed Networks},
author={S. Shalileh and B. Mirkin},
booktitle={International Conference on Intelligent Data Engineering and Automated Learning. Lecture Notes in Computer Science, vol 12490. },
pages={413--422},
year={2020},
address={Guimarães, Portugal}}
Or:
Shalileh S. & Mirkin B. (2020) A One-by-One Method for Community Detection in Attributed Networks. In: Analide C., Novais P., Camacho D., Yin H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Springer, Cham.