The modern world is awash in complex data that can contain the keys to improving our lives. The scope of this data has rapidly outpaced our capabilities to analyze and comprehend, so we turn to computers to help. However, state-of-the-art technology can only supplement the human element. People assist in each stage of data science, whether it's data cleaning, understanding algorithm design, exploring computed results, or collaborating and sharing for decision-making. To present complex information to humans, we use visualizations that leverage our extraordinary perceptual system which can detect trends, clusters, gaps, and outliers almost instantly.
A challenging and increasingly important type of data is networks of entities and their relationships. Networks have been are widely used across diverse disciplines to reason about complex behavior. These analyses involve understanding relationships, as well as associated attributes, statistics, or groupings. The omnipresent node-link visualization excels at showing topology and features simultaneously, but many are difficult to extract meaning from due to poor layout or shoehorning inherent complexity into limited space. The first part of my talk will detail techniques for measuring the readability of node-link visualizations and strategies to help users create more effective and understandable visualizations.
Moreover, analyses of complex data often requires several sessions, and when returning later it can be difficult to recall the steps in your workflow. Data science in many domains is also highly collaborative. Multiple analysts may be working alongside stakeholders with varying expertise and time constraints. The second part of my talk addresses these needs, and I introduce visualization strategies that assist in making analysis workflows repeatable, free of errors, understandable, and easily shareable.
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