The visual exploration of large and complex network structures remains a challenge for many application fields, such as systems biology or social sciences. Often, various domain experts would like to work together to improve the analysis time or the quality of the analysis results. Collaborative visualization tools can facilitate the analysis process in such situations. Moreover, a growing number of real world networks are multivariate and often interconnected with each other. Entities in a network may have relationships with elements of other related data sets, which do not necessarily have to be networks themselves, and these relationships may be defined by attributes that can vary greatly. A challenge is to correctly assign the attributes and relations between different data sets and graphs in order to be able to analyze them visually afterwards. The navigation between the resulting visualizations is also difficult. How can users be guided to other interesting data points relevant to their current view and how can this information be additionally displayed in a graph without losing the overview of the data?

In this dissertation, we propose our new web-based visualization environment OnGraX, which supports distributed, synchronous and asynchronous collaboration of networks and related multivariate data sets. In addition to standard collaboration features like event tracking or synchronizing, our client/server-based system provides a rich set of visualization and interaction techniques for better navigation and overview of the input network. Changes made by specific analysts or even just visited network elements can be highlighted by heat maps, which enable us to visualize user behavior data without affecting the original graph visualization. We evaluate the usability of the heat map approach against two alternatives in a user experiment.

Additional features of OnGraX include a comprehensive visual analytics approach that supports researchers to specify and subsequently explore attribute-based relationships across networks, text documents, and derived secondary data. Our approach provides an individual search functionality based on keywords and semantically similar terms over an entire text corpus to find related network nodes. For examining these nodes in the interconnected network views, we introduce a new interaction technique, called Hub2Go, which facilitates the navigation by guiding the user to the information of interest. To showcase these features, we use a large text corpus collected from papers listed in the IEEE VIS publications data set (1990--2015) that consists of 2,752 documents. We analyze relationships between various heterogeneous networks, a Bag-of-Words index, and a word similarity matrix, all derived from the initial corpus and metadata. We also propose a design for the interactive specification of degree-of-interest functions, which can be used to provide and evaluate configurations for guidance based on network attributes and logged user data in heterogeneous networks.