Graduate level
Spring 2024
Lectures: Monday, 14-17
Instructor: Dr. A. Abdollahpouri
Email: abdollahpouri@gmail.com ,
Course Description
This
course covers theory and modeling of real-world networks such as
computer, social, and biological networks where the underlying topology
is a dynamically growing complex graph.
Many phenomena in nature
can be modeled as a network and studied using network science.
Researchers from many areas including biology, computer science,
engineering, epidemiology, mathematics, physics, and sociology have
been studying complex networks of their field.
Scale-free networks
and small-world networks are well known examples of complex networks
where power-law degree distribution and high clustering are their
respective characteristic feature. These networks have been identified
in many fundamentally different systems. Complex networks display
non-trivial topological features that require an in depth study.
Textbooks
Networks: An Introduction M.E.J. Newman
Network Science: Interactive Textbook László Barabási
Information and Influence Propagation in Social Networks, Wei Chen, Laks V.S. Lakshmanan, Carlos Castillo
Social Media Mining, Reza Zafarani, Mohammad Ali Abbasi, Huan Liu
Networks, Crowds, and Markets: Reasoning About a Highly Connected World David Easley, Jon Kleinberg
Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, and Jeff Ullman
Lecture notes
| Introduction and overview | Lecture1 |
| Graph Theory (Mathematics of Networks) | Lecture2 |
| Centrality and Ranking | Lecture3 |
| Network Models (Random Graphs) | Lecture4 |
| Community Detection | Lecture5 |
| Diffusion models and Influence Maximization | Lecture6 |
| Link Prediction | Lecture7 |
| Network Robustness | Lecture8 |
Grading Policy
Homeworks……20% | |
Project .….. 25% | |
Final Exam…..50% | |
Class Participation.....5% |
Assignments
Homework1 Due date:Homework2 Due date:
Homework3 Due date: