Correlation networks are emerging as a powerful tool for modeling temporal mechanisms within the cell. Particularly useful in examining coexpression within microarray data, studies have determined that correlation networks follow a power law degree distribution and thus manifest properties such as the existence of "hub" nodes and semicliques that potentially correspond to critical cellular structures. Difficulty lies in filtering coincidental relationships from causative structures in these large, noise-heavy networks. As such, computational expenses and algorithm availability limit accurate comparison, making it difficult to identify changes between networks. In this vein, we present our work identifying temporal relationships from microarray data obtained from mice in three stages of life. We examine the characteristics of mouse networks, including correlation and node degree distributions. Further, we identify high degree nodes ("hubs") within networks and define their essentiality. Finally, we associate Gene Ontology annotations to network structures to deduce relationships between structure and cellular functions.