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

TL;DR

This paper introduces a simple, scalable visual technique for subgraph extraction using pivots and filters, clarifying user intent and enabling adaptive data abstractions in graph visualization.

Contribution

It presents a pivot-based visual method that is data-agnostic and scalable, along with insights into user intent ambiguity and potential for smart pivots.

Findings

The technique scales independently of graph size.

User intent ambiguity can be identified and addressed.

Potential for extending pivots into smart, intent-aware operations.

Abstract

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.