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Codebase for the Understanding the Structural Forces that Make Social Graphs Vulnerable to De-anonymization

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Graph_Unmasking

Python codebase to mount meachine-learning based de-anonymization attacks on social graphs, and explaining the success of such attack via network metrics

Getting Started

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.

Prerequisites

What things you need to install the software and how to install them

* snap
* pandas
* numpy
* itertools
* matplotlib
* sklearn
* imblearn

Installing

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

Built With

Contributing

Please follow the Github workflow process for submitting pull requests to us.

Authors

  • Sameera Horawalavithana - Initial work

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

  • Hat tip to anyone whose code was used
  • Inspiration
  • etc

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Codebase for the Understanding the Structural Forces that Make Social Graphs Vulnerable to De-anonymization

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