  This paper reports on a simple visual technique that boils extracting a
subgraph down to two operations---pivots and filters---that is agnostic to both
the data abstraction, and its visual complexity scales independent of the size
of the graph. The system's design, as well as its qualitative evaluation with
users, clarifies exactly when and how the user's intent in a series of pivots
is ambiguous---and, more usefully, when it is not. Reflections on our results
show how, in the event of an ambiguous case, this innately practical operation
could be further extended into "smart pivots" that anticipate the user's intent
beyond the current step. They also reveal ways that a series of graph pivots
can expose the semantics of the data from the user's perspective, and how this
information could be leveraged to create adaptive data abstractions that do not
rely as heavily on a system designer to create a comprehensive abstraction that
anticipates all the user's tasks.
