Complex networks are used to model real-world systems using sets of nodes and edges that represent elements and their interactions, respectively. Examples of such systems/networks include but are not limited to: electric power grids, airlines, social networks, the World Wide Web, the Internet, biological networks, etc. Typically, it is not possible to predict their collective behavior from their individual components. However understanding the mathematical description of these systems allows the prediction and possibly control of their behavior and evolution. Our research focuses on the use of mathematical and computational tools to explore these systems, and in particular model, analyze and predict their structure and dynamic evolving behavior, as well as addressing algorithmic aspects taking into account the very large size of these networks.