Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
Browse
2 results
Search Results
Item Measuring and improving the readability of network visualizations(2013) Dunne, Cody; Shneiderman, Ben A; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Network data structures have been used extensively for modeling entities and their ties across such diverse disciplines as Computer Science, Sociology, Bioinformatics, Urban Planning, and Archeology. Analyzing networks involves understanding the complex relationships between entities as well as any attributes, statistics, or groupings associated with them. The widely used node-link visualization excels at showing the topology, attributes, and groupings simultaneously. However, many existing node-link visualizations are difficult to extract meaning from because of (1) the inherent complexity of the relationships, (2) the number of items designers try to render in limited screen space, and (3) for every network there are many potential unintelligible or even misleading visualizations. Automated layout algorithms have helped, but frequently generate ineffective visualizations even when used by expert analysts. Past work, including my own described herein, have shown there can be vast improvements in network visualizations, but no one can yet produce readable and meaningful visualizations for all networks. Since there is no single way to visualize all networks effectively, in this dissertation I investigate three complimentary strategies. First, I introduce a technique called motif simplification that leverages the repeating patterns or motifs in a network to reduce visual complexity. I replace common, high-payoff motifs with easily understandable glyphs that require less screen space, can reveal otherwise hidden relationships, and improve user performance on many network analysis tasks. Next, I present new Group-in-a-Box layouts that subdivide large, dense networks using attribute- or topology-based groupings. These layouts take group membership into account to more clearly show the ties within groups as well as the aggregate relationships between groups. Finally, I develop a set of readability metrics to measure visualization effectiveness and localize areas needing improvement. I detail optimization recommendations for specific user tasks, in addition to leveraging the readability metrics in a user-assisted layout optimization technique. This dissertation contributes an understanding of why some node-link visualizations are difficult to read, what measures of readability could help guide designers and users, and several promising strategies for improving readability which demonstrate that progress is possible. This work also opens several avenues of research, both technical and in user education.Item Visualizing & Exploring Networks Using Semantic Substrates(2008-08-18) Aris, Aleks; Shneiderman, Ben; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Visualizing and exploring network data has been a challenging problem for HCI (Human-Computer Interaction) Information Visualization researchers due to the complexity of representing networks (graphs). Research in this area has concentrated on improving the visual organization of nodes and links according to graph drawing aesthetics criteria, such as minimizing link crossings and the longest link length. Semantic substrates offer a different approach by which node locations represent node attributes. Users define semantic substrates for a given dataset according to the dataset characteristics and the questions, needs, and tasks of users. The substrates are typically 2-5 non-overlapping rectangular regions that meaningfully lay out the nodes of the network, based on the node attributes. Link visibility filters are provided to enable users to limit link visibility to those within or across regions. The reduced clutter and visibility of only selected links are designed to help users find meaningful relationships. This dissertation presents 5 detailed case studies (3 long-term and 2 short-term) that report on sessions with professional users working on their own datasets using successive versions of the NVSS (Network Visualization by Semantic Substrates, http://www.cs.umd.edu/hcil/nvss) software tool. Applications include legal precedent (with court cases citing one another), food-web (predator-prey relationships) data, scholarly paper citations, and U. S. Senate voting patterns. These case studies, which had networks of up to 4,296 nodes and 16,385 links, helped refine NVSS and the semantic substrate approach, as well as understand its limitations. The case study approach enabled users to gain insights and form hypotheses about their data, while providing guidance for NVSS revisions. The proposed guidelines for semantic substrate definitions are potentially applicable to other datasets such as social networks, business networks, and email communication. NVSS appears to be an effective tool because it offers a user-controlled and understandable method of exploring networks. The main contributions of this dissertation include the extensive exploration of semantic substrates, implementation of software to define substrates, guidelines to design good substrates, and case studies to illustrate the applicability of the approach to various domains and its benefits.