GraphTrail: analyzing large multivariate, heterogeneous networks while supporting exploration history

  • Authors:

  • Cody Dunne;Nathalie Henry Riche;Bongshin Lee;Ronald Metoyer;George Robertson

  • Affiliations:

  • Microsoft Research, Redmond, WA & University of Maryland, Coloege Park, MD, United States;Microsoft Research, Redmond, Washington, United States;Microsoft Research, Redmond, Washington, United States;Oregon State University, Corvallis, Oregon & Microsoft Research, Redmond, WA, United States;Microsoft Research, Redmond, Washington, United States

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2012

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Abstract

Exploring large network datasets, such as scientific collaboration networks, is challenging because they often contain a large number of nodes and edges in several types and with multiple attributes. Analyses of such networks are often long and complex, and may require several sessions by multiple users. Therefore, it is often difficult for users to recall their own exploration history or share it with others. We introduce GraphTrail, an interactive visualization for analyzing networks through exploration of node and edge aggregates that captures users' interactions and integrates this history directly in the exploration workspace. To facilitate large network analysis, GraphTrail integrates aggregation with familiar charts, drag-and-drop interaction on a canvas, and a novel pivoting mechanism for transitioning between aggregates. Through a three-month field study with a team of archeologists and a qualitative lab study with ten users, we demonstrate the effectiveness of our design and the benefits of integrated exploration history, including analysis comprehension, insight discovery, and exploration recall.