Network Science

We live in a world where we are surrounded by systems that are incredibly complex, from the society, a collection of billions individuals, to communications systems, integrating billions of devices, from computers to cell phones. In fact, the existence of living beings in this planet depends on the ability of thousands of proteins to work together in a seamless fashion. Furthermore, our ability to comprehend our surroundings is heavily influenced by the activity of billions of neurons in our brain. Such complex systems can be represented as static or dynamic networks of many interacting components. These components are typically much simpler in terms of behavior or function than the overall system, implying that the additional complexity of the latter is an emergent network property. Network science is a new discipline that investigates the topology and dynamics of such complex networks, aiming to better understand the behavior, function and properties of the underlying systems. In this course, we will study algorithmic, computational, and statistical methods of network science, as well as its applications in solving real-world problems in communications, biology, sociology, and cyber security. The specific topics include network metrics, properties, and models, network querying and analytics, network dynamics, and distributed graph engines. Another pervasive goal of this course is to guide students into the future by presenting research that reveals the ?next big thing? in network science.