Topological structure of complex networks and its importance in diffusion

The availability of huge data, due to the tremendous storage capacity of modern computers has allowed the systematic collection, and high processing speed has permitted analysis on that data by researchers on a scale far larger than previously possible. Due to this, complex network formation has been seen and observed in many real and artificial complex systems. As these systems are very large and complex, we cannot get an understanding of these complex systems just by only examining the separate components which constitute these systems. Therefore, modeling the way these components are interconnected in a system is very important for understanding the system as a whole. Further, despite the enormous variation in their components, functions, and sizes, these networks are surprisingly similar in topology, leading to the conjecture that complex systems are governed by the ubiquitous self-organizing principle. In this research, we emphasize on the importance of heterogeneous topological structure of real-world complex networks and its importance in understanding the phenomenon of diffusion in these networks