In this dissertation, we introduce three techniques for network sciences. The first of these techniques is a series of new models for describing network growth. These models, called network specialization models, are built with the idea that networks grow by specializing the function of subnetworks. Using these models we create theoretical networks which exhibit well-known properties of real networks. We also demonstrate how the spectral properties are preserved as the models grow. The second technique we describe is a method for decomposing networks that contain automorphisms in a way that preserves the spectrum of the original graph. This method for graph (or equivalently matrix) decomposition is called an equitable decomposition. Previously this method could only be used for particular classes of automorphisms, but in this dissertation we have extended this theory to work for every automorphism. Further we explain a number of applications which use equitable decompositions. The third technique we describe is a generalization of network symmetry, called latent symmetry. We give numerous examples of networks which contain latent symmetries and also prove some properties about them