Networks are ubiquitous in our modern society, ranging from computer networks, social networks and electric power networks. Recent advances in cloud computing, Internet of Things, and social networks have connected cyber, physical and human worlds together to form heterogeneous, dynamic and complex networks that pose new challenges in their control and optimization. This course will introduce the tools for the study of cyber-physical-human networks with the applications to the Internet congestion control, robotic and vehicular networks, mean-field epidemics, and critical infrastructures. It will show how certain common principles permeate the functioning of these diverse networks and how the same issues related to connectivity, robustness, stability, and strategic behaviors arise in several different types of networks. This course provides a unifying system theory to bridge different areas of applications to address systems that consist of social, cyber and physical components through the lens of networks.
The course is offered as a graduate level course. To follow the course, familiarity with dynamic systems (at the level of EE 3064), some background in probability theory (at the level of MA 3012) are required. Some familiarity with the basics of linear and nonlinear programming is desirable but not required. A minimum GPA of 3.2 is required for undergraduates to take the course.
[SY] R. Srikant and L. Ying, Communication Networks, Cambridge University Press, 2013
[FM] B. A. Francis and M. Maggiore, Flocking and Rendezvous in Distributed Robotics, SpringerBriefs in Electrical and Computer Engineering, Springer, 2015.
[MJ] M. Jackson, Social and Economic Networks, Princeton University Press, 2010
[MO] M. Osborne, Introduction to Game Theory, Oxford University Press, 2003.
[EK] D. Easley and J. Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World, Cambridge University Press
[EK] E. Estrada & P. Knight, A first course in network theory. Oxford University Press, 2015.
[BH] Bruce Hajek, Random Processes for Engineers, Cambridge University Press, 2015.
[NB] N. Biggs, Algebraic graph theory. Cambridge university press, 1993.
[RB] R. Gibbons, Game Theory for Applied Economists, Princeton University Press, 1992.
[ME] M. Mesbahi, M. Egerstedt, Graph Theoretic Methods in Multiagent Networks, Princeton University Press, 2010
[DB] D. P. Bertsekas, Network Optimization: Continuous and Discrete Models, Athena Scientific, 1998, Available at http://web.mit.edu/dimitrib/www/netbook_Full_Book.pdf
[LB] Y.Y. Liu, and Albert-Laszló Barabási. “Control Principles of Complex Networks,” arXiv preprint, 2015. Available at http://arxiv.org/abs/1508.05384
[SM] S. Meyn, Control Techniques for Complex Networks, Cambridge University Press, 2008.
Lecture 1: Introduction and basics
Lecture 2: Network utility optimization I: Distributed optimization
Lecture 3: Network utility optimization II: Stability and delay
Lecture 4: Markov chains and queuing networks
Lecture 5: Markov chains and queuing networks
Lecture 6: Large deviation and heavy-traffic approximation
Lecture 7: Robot rendezvous problems
Lecture 8: Opinion dynamics and consensus
Lecture 9: Branching processes and phase transitions (HW4)
Lecture 10: Complex networks
Lecture 11: Strategic networks
Lecture 12: Network formation
Lecture 13: Matching networks
Lecture 14: Epidemics over networks