Visualizing multi-level structures in data

Visualization is an important tool to analyze data, but there emerge various challenges from complex analytical data and tasks. In this dissertation, I present four projects that were motivated by these challenges, situated in the nested model proposed by Munzner, which consists of four layers to describe the components in visualization: domain, data and task, encoding design, and algorithm. In ADVIEW, to address the challenges of comparing many phylogenetic trees in the domain of biology, I propose a visual encoding to compress a tree representation, design and implement a multi-view interactive tool to handle the multiple levels of detail in a tree collection dataset, ranging from the whole collection, through subsets of trees, individual trees, subtrees, to leaf nodes. In SPRAWLTER, to address the existing visual encoding problems of readability metrics for node-link graphs, I propose two novel metrics to measure a finer-grained clutter and to balance the geometric sparseness and clutter. These metrics recognize different levels of visual saliency such as metanodes and leaf nodes in multi-level graphs. In LOGSEG, to fulfill user demands for chunking actions in the domain of image editing software, I propose a segmentation model for the action logs to serve the demands that require different chunking granularities. For example, smart undo for going back to a previous user task needs a low-level chunking, while managing an overview of milestones needs a high-level one. In CORGIE, to fill the gap in visual qualitative evaluation of graph neural networks (GNNs) in the domain of machine learning, I propose an approach and design a tool to explore correspondences between a graph and its embedding to check how different levels of structures are preserved from the input graph to the output embedding. I also design a new graph layout to reveal how a GNN leverages node neighbors and computes an embedding. I identify a common theme among these projects: multi-level structures. They consist of nesting subsets of data points that are relevant to the analytical tasks. I demonstrate how to exploit them in the visualization if provided in hierarchical data, or to compute them for non-hierarchical data.Science, Faculty ofComputer Science, Department ofGraduat