| Network structures are prevalent in many disciplines. Analysis of these networks can lead to many kinds of domain-specific insights. Many networks contain static information, but some are constantly evolving making analysis hard. Even datasets that are not naturally networks can be transformed into networks which can reveal interesting patterns. And some networks have relatively simple structures, but have underlying temporal activity that can be difficult to analyze through traditional methods. This dissertation research presents new methods for time and space efficient visualization of traditional and time varying networks, methods for transforming sets of time-series data into networks that can be analyzed through these methods, and methods for visualizing the temporal activity of complex yet topologically static networks. |
