Complex networks are structurally non-trivial and require a large set of tools to analyze their characteristics. In this thesis, we implement a standard statistical ovariance method to analyze local network structure. In addition, we implement the data-driven methods: Dynamic Mode Decomposition (DMD) and Kernel Dynamic Mode Decomposition (KDMD). These methods are grounded in Koopman theory and give us a dynamical systems perspective into network development. With feature matrices built from snapshots of motif counts throughout a network’s development, we characterize the dynamic behavior of the local network structure. Through DMD and KDMD, we identify sets of DMD and KDMD modes. Analyzing the modes, we identify spatiotemporal coherent structures in the data. The DMD and KDMD algorithms produce modes of low error, which are good approximations to true Koopman modes.