Python codebase to mount meachine-learning based de-anonymization attacks on social graphs, and explaining the success of such attack via network metrics
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
What things you need to install the software and how to install them
* snap
* pandas
* numpy
* itertools
* matplotlib
* sklearn
* imblearn
A step by step series of examples that tell you how to get a development env running, For the attack model:
cd scripts/
./run_attack_model.sh <graph name> <synthetic graph name>
And repeat
./run_attack_model.sh fb107 fb107
./run_attack_model.sh caGrQc caGrQc
./run_attack_model.sh soc-anybeat soc-anybeat
./run_attack_model.sh soc-gplus soc-gplus
./run_attack_model.sh wikinews wikinews
For the causality model:
ipython causality_model/Pearlian_DAG.ipynb
Please follow the Github workflow process for submitting pull requests to us.
- Sameera Horawalavithana - Initial work
This project is licensed under the MIT License - see the LICENSE.md file for details
- Hat tip to anyone whose code was used
- Inspiration
- etc