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This paper reports on a simple visual technique that boils extracting asubgraph down to two operations---pivots and filters---that is agnostic to boththe data abstraction, and its visual complexity scales independent of the sizeof the graph. The system's design, as well as its qualitative evaluation withusers, clarifies exactly when and how the user's intent in a series of pivotsis ambiguous---and, more usefully, when it is not. Reflections on our resultsshow how, in the event of an ambiguous case, this innately practical operationcould be further extended into "smart pivots" that anticipate the user's intentbeyond the current step. They also reveal ways that a series of graph pivotscan expose the semantics of the data from the user's perspective, and how thisinformation could be leveraged to create adaptive data abstractions that do notrely as heavily on a system designer to create a comprehensive abstraction thatanticipates all the user's tasks.

Jacob's Ladder: The User Implications of Leveraging Graph Pivots