Visualizing large real-world networks, such as social networks and scientific collaboration networks, is challenging not only because they contain large numbers of nodes and links but also due to their multivariate nature. Applications that analyze such datasets tend to focus on problems related to visualizing either multiple attributes on nodes or the topology of the network. Very few applications focus on both. This research explores a new approach to visualize such multivariate networks, using a glyph designed based on sunburst chart to encode attributes on the nodes and a combination of a treemap layout and a suitable graph layout to control the topology. We show the results of this approach by creating a collaboration network of researchers using a publications dataset that comprises references to all research papers published by users of the Argonne Leadership Computing Facility in the last three years. The goal of this visualization is to show a holistic view of the scholarly work from a research facility, which in turn helps to identify research groups and the researchers acting as bridges among them.