Jacob's Ladder: The User Implications of Leveraging Graph Pivots
This addresses the challenge of user interaction with graph data for analysts or system designers, offering incremental improvements in clarity and adaptability.
The paper tackles the problem of extracting subgraphs from complex data by introducing a visual technique based on pivots and filters that is data-agnostic and scales independently of graph size. The result includes a qualitative user evaluation that clarifies when user intent in pivots is ambiguous or not, and suggests extensions like 'smart pivots' and adaptive data abstractions.
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.