Spatio-temporal network is defined by a set of nodes, and a set of edges, where the properties of nodes and edges may vary over time. Such networks are encountered in a variety of domains ranging from transportation science to sensor data analysis. Given a spatio-temporal network, the aim is to develop a model that is simple, expressive and storage efficient. The model must also provide support for the design of algorithms to process frequent queries that need to be answered in the application domains. This problem is challenging due to potentially conflicting requirements of model simplicity and support for efficient algorithms. Time expanded networks which have been used to model dynamic networks employ replication of the network across time instants, resulting in high storage overhead and algorithms that are computationally expensive. This model is generally used to represent time-dependent flow networks and tends to be application-specific in nature. In contrast, the proposed time-aggregated graphs do not replicate nodes and edges across time; rather they allow the properties of edges and nodes to be modeled as a time series. Our approach achieves physical data independence and also addresses the issue of modeling spatio-temporal networks that do not involve flow parameters. In this paper, we describe the model at the conceptual, logical and physical levels. We also present case studies from various application domains.