Influence Maximization in Social Networks (1st Offering)
Presenter: Farzaneh Kazemzadeh
Date: Wednesday, July 30th, 2025
Time: 12:00 PM
Location: 4th Floor - 300 Ouellette Ave., School of Computer Science, Advanced Computing Hub
This workshop focuses on the Influence Maximization problem in social networks, which aims to identify key nodes capable of effectively spreading information throughout the network. Influence propagation plays a crucial role in various real-world applications, including viral marketing, news dissemination, rumour spreading, and epidemic modeling.
Participants will be introduced to the core principles of influence maximization, explore foundational solution methods, and analyze classical algorithms such as the greedy approach. The session provides a comprehensive understanding of how influence spreads and how it can be modeled and optimized.
The session lays essential groundwork for understanding how influence propagates and how it can be effectively modeled, analyzed, and optimized in large-scale networks.
- Detailed overview of the Influence Maximization problem in social networks
- Exploration and analysis of fundamental and widely used algorithms proposed for solving the problem
- Critical evaluation of the strengths and limitations of existing approaches
- Comparative discussion of algorithmic efficiency, scalability, and applicability
- Development of an intuitive and applicable understanding of how influence spreads and how it can be optimized
- Providing participants with a broader perspective on solving Influence Maximization using computational models
- Primary familiarity with Internet and Social Networks concepts.
- Introductory understanding of graphs and graph theory.
Farzaneh is a Ph.D. student and research assistant who started her program at the School of Computer Science in the Summer of 2024. Her main research areas are Social Networks Analysis and Mining, Influence Maximization, Data Mining and Artificial Intelligence.