Multi-Objective Optimization in Social Networks: A Knowledge-Driven Computational Approach
This presentation begins by reviewing the fundamentals and key characteristics of social networks, along with primary challenges such as community detection, link prediction, and influence maximization. We then explore how these problems can be framed as multi-objective optimization tasks that require balancing multiple goals simultaneously. To address these problems, a knowledge-driven computational approach is presented that guides the optimization process by leveraging various sources of knowledge extracted from the network structure. Finally, we discuss the effectiveness of this approach in handling the complex and dynamic nature of social networks.
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- 4th Floor, 300 Ouellette Ave.
- Windsor, Ontario
- Canada
- Building: School of Computer Science, Advanced Computing Hub
- Starts 10 July 2025 04:00 AM UTC
- Ends 24 July 2025 01:45 PM UTC
- No Admission Charge
Speakers
Pooya Moradian Zadeh of University of Windsor
Multi-Objective Optimization in Social Networks: A Knowledge-Driven Computational Approach
Biography:
Dr. Pooya Moradian Zadeh is an associate professor and a researcher in the School of Computer Science at the University of Windsor. His research interests focus on data analytics and the modeling and optimization of complex social systems such as healthcare using computational intelligence and social network analysis techniques. He has successfully led several software development teams and projects in the areas of data analytics, applied artificial intelligence, and health informatics. Dr. Moradian Zadeh is the recipient of the 2024 Alumni Award for Distinguished Contributions to University Teaching, the 2020 Roger Thibert Teaching Excellence Award, and the 2019 Student Engagement Award for Excellence in Teaching Achievements and Educational Leadership from the University of Windsor.
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