Many real world problems can be modeled as heterogeneous graphs where nodes and/or links are of different types. And these graphs are often dynamically changing. One example is the bibliographic database, where authors and research topics are different types of entities, and the graph changes over time as the researchers switch their interests or form new collaborative relations. Research in the area of graph visualization has been concerned with designing novel and effective visual encoding schemes and user interactions for the viewers to gain insight into graph data. We follow this line of research and this thesis reports our work in developing visual analysis techniques for heterogeneous and dynamic graph data from various application domains.

In the first work, we visualiz...[ Read more ]

Many real world problems can be modeled as heterogeneous graphs where nodes and/or links are of different types. And these graphs are often dynamically changing. One example is the bibliographic database, where authors and research topics are different types of entities, and the graph changes over time as the researchers switch their interests or form new collaborative relations. Research in the area of graph visualization has been concerned with designing novel and effective visual encoding schemes and user interactions for the viewers to gain insight into graph data. We follow this line of research and this thesis reports our work in developing visual analysis techniques for heterogeneous and dynamic graph data from various application domains.

In the first work, we visualize heterogeneous graph data that not only records the relationship among people, but also the various items they are related to (e.g. interested topics or music). We design visualizations that can help to study if people closely linked have similar items of interest, and introduce a novel set visualization technique and the corresponding layout algorithm to display the overlap of their interests. The techniques are applied to a bibliographic dataset and the user data from a social music service website.

The second work studies the dynamic interplay among topics, opinion leaders and the audiences on social media. More specifically, we propose a combination of time series modeling and interactive visualization techniques to study how various topics compete to attract public attention when they are spreading on social media (e.g. Twitter), and what roles do opinion leaders such as mass media, political figures and grassroots play in the rise and fall of various topics. In the experiment, we report the insights gained on collections of Tweets.

The third study proposes a visualization technique to explore network dynamics, especially how the new edges are formed through the assortative and relational mechanisms, which have been observed in the evolution of many networks. The visualization technique developed not only displays the structural evolution of a dynamic network, but also allows the viewer to explore the various mechanisms underlying the changes. The techniques are demonstrated through the visual analysis of real-world datasets: the co-authorship network and the user interaction graph on social websites.