This talk covers theory and modeling of real-world networks such as computer, social, and biological networks where the underlying topology is a dynamically growing complex graph. Many phenomena in nature can be modeled as a network and studied using network science. Researchers from many areas including biology, computer science, engineering, epidemiology, mathematics, physics, and sociology have been studying complex networks of their field. Scale-free networks and small-world networks are well known examples of complex networks where power-law degree distribution and high clustering are their respective characteristic feature. These networks have been identified in many fundamentally different systems. Complex networks display non-trivial topological features that require an in depth study.
