Visualizing & Exploring Networks Using Semantic Substrates
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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.