As more and more data is created each day, researchers from different science domains are trying to make sense of it. A lot of this data, for example our connections to friends on different social networking websites, can be modeled as graphs, where the nodes are actors and the edges are relationships between them. Researchers analyze this data to find new forms of communication, to explore different social groups or subgroups, to detect illegal activities or to seek for different communication patterns that could help companies in their marketing campaigns. Another example are huge networks in system biology. Their visualization is crucial for the understanding of living beings. The topological structure of a network on its own could give insight into the existence or distribution of interesting actors in the network. However, this is often not enough to understand complex network systems in real-world applications. The reason for this is that all the network elements (nodes or edges) are not simple one-dimensional data. For instance in biology, experiments can be performed on biological networks. These experiments and network analysis approaches produce additional data that are often important to be analyzed with respect to the underlying network structure. Therefore, it is crucial to visualize the additional attributes of the network while preserving the network structure as much as possible. The problem is not trivial as these so-called multivariate networks could have a high number of attributes that are related to their nodes, edges, different groups, or clusters of nodes and/or edges. The aim of this thesis is to contribute to the development of different visualization and interaction techniques for the visual analysis of multivariate networks. Two research goals are defined in this thesis: first, a deeper understanding of existing approaches for visualizing multivariate networks should be acquired in order to classify them into categories and to identify disadvantages or unsolved visualization challenges. The second goal is to develop visualization and interaction techniques that will overcome various issues of these approaches. Initially, a brief survey on techniques to visualize multivariate networks is presented in this thesis. Afterwards, a small task-based user study investigating the usefulness of two main approaches for multivariate network visualization is discussed. Then, various visualization and interaction techniques for multivariate network visualization are presented. Three different software tools were implemented to demonstrate our research efforts. All features of our systems are highlighted, including a description of visualization and interaction techniques as well as disadvantages and scalability issues if present.

This work is an interdisciplinary study involving mainly the fields of information visualisation and human-computer interaction. The advancement of technology has expanded the ways in which humans interact with machines, which has benefited both the industry as well as several fields within science. However, scientists and practitioners in the information visualisation domain remain working, mostly, with classical setups constituted of keyboard and standard computer mouse devices. This project investigates how a shift in the human-computer interaction aspect of visualisation software systems can affect the accomplishment of tasks and the overall user experience when analysing two-dimensionally displayed multivariate networks. Such investigation is relevant as complex network structures have seen an increase in use as essential tools to solve challenges that directly affect individuals and societies, such as in medicine or social sciences. The improvement of visualisation software’s usability can result in more of such challenges answered in a shorter time or with more precision. To answer this question, a web application that enables users to analyse multivariate networks through interfaces based both on hand gesture recognition and mouse device was developed. Also, a number of gesture designs were developed for several tasks to be performed when visually analysing networks. Then, an expert in the field of human-computer interaction was invited to review the proposed hand gestures and report his overall user experience of using them. The results show that the expert had, overall, similar user experience for both hand gestures and mouse device. Moreover, the interpretation of the results indicates that the accuracy offered by gestures has to be carefully taken into account when designing gestures for selection tasks, particularly when the selection targets are small objects. Finally, our analysis points out that the manner in which the software’s graphical user interface is presented also affects the usability of gestures, and that both factors have to be designed accordingly.