Over the past decade, there has been a growing interest for the complex « connectedness » of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication.
now takes place. Beyond this classical example, the Network science is a now thriving and increasingly important cross-disciplinary domain that focuses on the representation, analysis, and modeling of various connected systems such as social network, brain Networks, biological network, mobility and transport networks. Motivated by these developments in the world, there has been a coming-together of multiple scientific disciplines in an effort to understand how highly connected systems operate. Network science aims to capture, modeling and understanding networks and rich data requires understanding both the mathematics of networks and the computational tools for identifying and explaining the patterns they contain.
This graduate-level course will examine modern techniques for analyzing and modeling the structure and dynamics of complex networks. The focus will be on statistical algorithms and methods, and both lectures and assignments will emphasize model interpretability and understanding the processes that generate real data. Applications will be drawn from computational biology and computational social science. No biological or social science training is required.
Dr Vincent Gauthier
Parcours de rattachement
Pour les étudiants du diplôme Computer Science for Networks M2
- Introduction and overview
- Network basicks
- Centrality measures
- Eigen centrally page rank
- Random graphs (simple)
- Configuration models
- Advanced random graph model
- Network resiliency
- Spreading processes
- Social Network analysis
- Community detection on networks
- Data wrangling + data sampling
- Student project presentations
The final grades will be weighted as follows:
Student project: 20%
Final Exam: 60%