Hybrid Multivariate Network Visualization Combining Dimensional Projection and Graph Drawing

The goal of information visualization is to transform abstract data into efficient visual format which help humans extract useful information from the data collected. Two distinctive types of data are of particular interest to the information visualization community: one is multidimensional data in which each data point/entity is described by various attributes. The other is relational data which represent the interaction between the entities. These two types of data are common in the real world but difficult for people to understand. Many visualization approaches have been developed to help people gain insight in to such data sets. This thesis focuses on visualizing the multivariate networks which contains the aforementioned two types of data. The word “multivariate” has two meanings: 1) The entities have multiple attributes, 2) The same set of entities can have different relationships. Visualizations should help viewers to compare attributes of different entities, recognize entities’ roles in various relationships and detect correlation between attributes and relationships. We present a two-phase approach to visualize the multivariate networks. In the first phase, vertices with similar attribute values and short graph distance are mapped to proximate regions on the representation. In the second phase, the layout generated in the first phase is adjusted to separate overlapping vertices, reduce vertex-edge overlaps and edge crossings. A user study is carried out to evaluate the effectiveness of this hybrid approach. Results are compared against the commonly used glyph-based technique. The user study shows that the hybrid approach helps users to find correlations between the vertices’ attribute and relationship. In addition, the users find the hybrid visualization facilitates comparing different relationships between the same set of entities. Finally, the hybrid approach is applied to visualize four international trading networks.