Visualizing network data, from tree structures to arbitrarily connected graphs, is a difficult problem in information visualization. A large part of the problem is that in network data, users not only have to visualize the attributes specific to each data item, but also the links specifying how those items are connected to each other. Past approaches to resolving these difficulties focus on zooming, clustering, filtering and applying various methods of laying out nodes and edges. Such approaches, however, focus only on optimizing a network visualization in a single view, limiting the amount of information that can be shown and explored in parallel. Moreover, past approaches do not allow users to cross reference different subsets or aspects of large, complex networks with each other. In this paper, we propose an approach which uses multiple coordinated views of a given network. To illustrate our approach, we implement a tool called DualNet and evaluate the tool with a case study using an email communication network. We show how using multiple coordinated views improves navigation and provides insight into large networks with multiple node and link types.