Project: Fairness-aware Influence Minimization for Combating Fake News
Description
Influence blocking and fake news mitigation have been the main research direction for the network science and data mining research communities in the past few years. Several methods have been proposed in this direction [1]. However, none of the proposed solutions has proposed feature-blind method that is fair for each individual community. In this project, we will focus on proposing feature-blind solutions for fairness-aware influence blocking techniques.
In the influence blocking problem, we focus on finding a smallest set of nodes whose immunization will minimize information spread over the network. This is referred to as influence minimization or influence blocking problem.
We would like to propose a feature-blind solution that considers both the minimization of the objective function for fake news spread as well as maximize the fairness objective with respect to the communities while influence blocking.
*Feel free to contact me to know more details about this project.
References:
- Saxena, Akrati, Pratishtha Saxena, and Harita Reddy. "Fake News Propagation and Mitigation Techniques: A Survey." In Principles of Social Networking, pp. 355-386. Springer, Singapore, 2022.
- Stoica, Ana-Andreea, Jessy Xinyi Han, and Augustin Chaintreau. "Seeding network influence in biased networks and the benefits of diversity." In Proceedings of The Web Conference 2020, pp. 2089-2098. 2020.
Stoica, Ana-Andreea, and Augustin Chaintreau. "Fairness in social influence maximization." In Companion Proceedings of The 2019 World Wide Web Conference, pp. 569-574. 2019.
Details
- Supervisor
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Mykola Pechenizkiy
- Secondary supervisor
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Akrati Saxena
- Interested?
- Get in contact