Time series analysis of dynamic networks is an important area of study that helps in predicting changes in networks. Changes in networks are used to analyze deviations in the network characteristics. This analysis helps in characterizing any network that has dynamic behavior. This area of study has applications in many domains such as communication networks, climate networks, social networks, transportation networks, and biological networks. The aim of this research is to analyze the structural characteristics of such dynamic networks. This thesis examines tools that help to analyze the structure of the networks and explores a technique for computation and analysis of a large climate dataset. The computations for analyzing the structural characteristics are done in a computing cluster and there is a linear speed up in computation time compared to a single-core computer. As an application, a large sea ice concentration anomaly dataset is analyzed. The large dataset is used to construct a correlation based graph. The results suggest that the climate data has the characteristics of a small-world graph.