We are surrounded by systems that are far more complicated than we imagine: they are either composed of many interconnected components or characterized by intricate dynamics, such as our society, a biological system like a human being, the communication infrastructure, the neuron system in our brain, and more. Such systems are collectively called complex systems. Network science, as an emerging interdisciplinary filed, is developed under the need to describe, understand, predict and eventually control these complex systems. Indeed, behind each complex system there is an intricate network that encodes the interactions between the system’s components. Hence, network science provides us general yet powerful tools in the investigation of complex systems.

The aim of this course is to bring students the basic concepts, methodologies as well as recent advances in network science and complex systems. Upon completion of this class, the successful students are expected to be able to

• Explain basic metrics and measures used to characterize networks

• Analyze a network using the various measures and a suitable network analysis software tool

• Discuss the strengths and weaknesses of random graph models

• Understand and apply key algorithms for node ranking, community identification, and network comparison

• Understand and apply models and theories used to reason about cascading behaviors, information diffusion, contagion, and decentralized navigation in networks

• Understand and explain the interdisciplinary nature of the area of network science

• Critique research papers in the area

• Apply knowledge gained in the course to carry out a project and write a scientific report