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Compound graphs are networks in which vertices can be grouped into larger subsets, with these subsets capable of further grouping, resulting in a nesting that can be many levels deep. In several applications, including biological workflows, chemical equations, and computational data flow analysis, these graphs often exhibit a tree-like nesting structure, where sibling clusters are disjoint. Common compound graph layouts prioritize the lowest level of the grouping, down to the individual ungrouped vertices, which can make the higher level grouped structures more difficult to discern, especially in deeply nested networks. Leveraging the additional structure of the tree-like nesting, we contribute an overview+detail layout for this class of compound graphs that preserves the saliency of the higher level network structure when groups are expanded to show internal nested structure. Our layout draws inner structures adjacent to their parents, using a modified tree layout to place substructures. We describe our algorithm and then present case studies demonstrating the layout's utility to a domain expert working on data flow analysis. Finally, we discuss network parameters and analysis situations in which our layout is well suited. 

Two people looking at the same dataset will create different mental models, prioritize different attributes, and connect with different visualizations. We seek to understand the space of data abstractions associated with mental models and how well people communicate their mental models when sketching. Data abstractions have a profound influence on the visualization design, yet it’s unclear how universal they may be when not initially influenced by a representation. We conducted a study about how people create their mental models from a dataset. Rather than presenting tabular data, we presented each participant with one of three datasets in paragraph form, to avoid biasing the data abstraction and mental model. We observed various mental models, data abstractions, and depictions from the same dataset, and how these concepts are influenced by communication and purpose-seeking. Our results have implications for visualization design, especially during the discovery and data collection phase. 

Computing professionals in areas like compilers, performance analysis, and security often analyze and manipulate control flow graphs (CFGs) in their work. CFGs are directed networks that describe possible orderings of instructions in the execution of a program. Visualizing a CFG is a common activity in developing or debugging computational approaches that use them. However, general graph drawing layouts, including the hierarchical ones frequently applied to CFGs, do not capture CFG-specific structures or tasks and thus the resulting drawing may not match the needs of their audience, especially for more complicated programs. While several algorithms offer flexibility in specifying the layout, they often require expertise with graph drawing layouts and primitives that these potential users do not have. To bring domain-specific CFG drawing to this audience, we develop CFGConf, a library designed to match the abstraction level of CFG experts. CFGConf provides a JSON interface that produces drawings that can stand-alone or be integrated into multi-view visualization systems. We developed CFGConf through an interactive design process with experts while incorporating lessons learned from previous CFG visualization systems, a survey of CFG drawing conventions in computing systems conferences, and existing design principles for notations. We evaluate CFGConf in terms of expressiveness, usability, and notational efficiency through a user study and illustrative examples. CFG experts were able to use the library to produce the domain-aware layouts and appreciated the task-aware nature of the specification.