Do boxes affect exploration behavior and performance in group-in-a-box layouts?

The group-in-a-box (GIB) layout is an efficient graph-drawing method designed to visualize the group structure of graphs. The layout communicates group sizes and both within-group and between-group network structures simultaneously. The layout is characterized by its composition of multiple elements, including nodes, edges, and boxes. However, there is limited empirical guidance on how these elements should be combined. In this paper, we measured participants’ task performance and eye movements while identifying the group with the largest number of internal edges. We investigated the effect of visualization elements on task performance while controlling the density of internal edges and the box size. The results revealed that the box size in a GIB layout significantly affects the task accuracy either positively or negatively while eye-tracking data suggest that participants focused on internal edges, not the box size. These findings contribute empirical guidance for GIB layout design and lay the groundwork for future research as GIB layout becomes more widely used.