Influence Maximization in Social Networks (2nd Offering)
Presenter: Farzaneh Kazemzadeh
Date: Thursday, July 24th, 2025
Time: 1:00 PM
Location: 4th Floor - 300 Ouellette Ave., School of Computer Science, Advanced Computing Hub
This workshop offers a foundational introduction to Influence Maximization in Social Networks.
It begins with key concepts from graph theory and the structural characteristics of social networks.
The session then presents the influence maximization problem in depth, including the classical greedy algorithm, the influence spread function, and other related theoretical notions.
Participants will explore how this problem is applied in real-world scenarios and will gain insight into leveraging computational models to optimize the spread of information, ideas, or innovations across social networks.
The session lays essential groundwork for understanding how influence propagates and how it can be effectively modelled, analyzed, and optimized in large-scale networks.
- Introduction to Social Networks and Social Network Analysis (SNA)
- Introduction to Graph Theory and Its Concepts
- Definition of the Influence Maximization Problem, the Basic Greedy Algorithm, and Other Related Concepts
- Examination of How Influence Spreads in Social Networks
- Definition of the Importance and Role of Influence Maximization in Social Networks and Its Real-World Applications
- Primary familiarity with the Internet and Social network concepts.
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.