T Technical Teaching
- Some of the most interesting systems in today's cyber-world are large networks with complex structure and dynamics. Some examples are the Internet (wired and wireless), online social networks (e.g. Facebook, Twitter), peer-to-peer networks (e.g. Skype, BitTorrent), wireless mesh and sensor networks, etc.
- This course will teach students how to analyze (a) the structure of large networks, and (b) the performance of dynamic processes over these networks (e.g. routing, broadcasting, searching, virus spread).
- The end goal is to understand the common underlying properties and their implications for the design of efficient algorithms for large networks.
- The course consists of three main parts (stochastic processes, complex network models, dynamics over networks) each comprising 3-4 modules. Each module will first introduce the necessary analytical background (e.g. markov chains, scale-free graphs), and then present an application of this theory to a real networking problem drawn from diverse topics in networking (e.g. network traffic analysis, mobility modeling, etc.).
- Part I (Stochastic Processes): This part will provide a basic introduction to key stochastic processes needed for the remainder of the course, covering topics such as poisson processes, renewal processes, and markov chains.
- Part II (Complex Network Models): This part will introduce some key random graph models and properties. Topics include Erdos-Renyi graphs, scale-free graphs, small-world graphs, preferential attachment, clustering and communities, degree distributions.
- Part III (Network Dynamics): This part will cover random processes and dynamics over complex networks, such as diffusion, random walks, searching, epidemic models, etc.
- Part IV (Applications): Most of the above topics will be presented along with some important applications drawn from areas like, Internet measurement, searching in peer-to-peer (P2P) networks, routing and broadcasting, sampling of social networks, etc.