In previous chapters, this book has primarily concerned itself with visualization methods for static, multivariate graphs. In a static scenario, the network has a number of attributes associated with its elements. These attribute values remain fixed and the challenge is to visualize the interactions between the network(s) and these attributes. Static multivariate graphs could be viewed as graphs with an associated high dimensional data set linked to its elements. Time is simply another dimension in this multivariate data set that can interact with the vertices, edges, and attribute values of the network. However, humans perceive time differently as we know from our everyday interactions with the physical world. Thus, intuitively, this dimension is often handled differently when supporting the presentation of data that changes over time. Visualization applications and techniques have, and probably should, continue to exploit this fact, allowing for effective visualization methods of temporal multivariate graphs.