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EE6605 Complex Networks: Modeling, Dynamics and Control 
Lecturer: Prof Guanrong (Ron) Chen


Offered: SemB in 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2019, 2020 

Course Aims and Objectives:
This course will study the emerging topic of complex networks, especially those regarded as evolving and dynamical, i.e. huge and diverse in many respects. Complex networks such as the Internet, WWW, transportation networks, power grids, biological neural networks, and scientific cooperation networks of all kinds provide challenges for future technological development. In particular, advanced societies have become dependent on large infrastructural networks to an extent beyond our capability to plan (modeling) and to operate (control). The recent spate of collapses in power grids and ongoing virus attacks on the Internet illustrate the need for knowledge about modeling, analysis of behaviors, optimized planning and performance control in such networks. For advancement of techniques, it has become clear that more fundamental knowledge will be needed in an engineering context about how dynamical networks emerge, behave, adapt, selforganize, evolve, and how they can be controlled. The aim is to present the status of these topics and some basic techniques of various complex dynamical networks.
Intended Learning Outcomes
Units: 3 Level: P6 Medium of Instruction: English
Syllabus:
Graph Theory Fundamentals
Basic concepts and properties, Eulerian graphs, Hamiltonian graphs, applications (minimum connector problem, Chinese postman problem, shortest path problem), directed networks
Recent advances in scientific literature including the complexity of models, degree distributions, random graphs, smallworld characteristics, scalefree features, nevigability, community structures, and network modeling
Network Dynamics
Network dynamical behaviors; network stability; network games; community formation and detection; human opinion dynamics; network evolution and control
Internet topology; network evolution; information flows; epidemics spreading; cascade failures; network search; network stability
Network Synchronization and Control
Network synchronization phenomena and criteria; network stabilization and pinning control; design and optimization of network topologies
Potential Applications
Internet; power grids; transportation; human behavioral dynamics; physical, economic and social networks
Teaching Methods:
Lecturing is the core of teaching. Lectures may
involve presentation Reading materials will be assigned. One homework assignment will be given in the first few weeks followed by a onemonth computer project assignment.
Teaching Pattern:
Assessment Pattern: 50% Course Work (homework 10%; Midterm Test 20%; projects 20%); 50% Final Exam (closedbook exam) Test and Exam Duration: 2 hours (closedbook)
Requirements: For a student to pass the course, according to the university guidelines, at least 30% of the maximum mark for the course work and also 30% of the maximum mark for the final exam must both be obtained.
Prerequisites: Nil Precursor: Nil Equivalent Courses: Nil
Booklist: References (recommended but not required) [1] G. Chen, X. Wang and X. Li, Introduction to Complex Networks: Models, Structures and Dynamics, Higher Education Press, Beijing, 2012. [2] X. Wang, X. Li and G. Chen, Network Science: An Introduction (in Chinese), Higher Education Press, Beijing, 2012.
