Evaluating Perceptually Complementary Views for Network Exploration Tasks

We explore the relative merits of matrix, node-link and combined side-by-side views for the visualisation of weighted networks with three controlled studies: (1) finding the most effective visual encoding for weighted edges in matrix representations; (2) comparing matrix, node-link and combined views for static weighted networks; and (3) comparing MatrixWave, Sankey and combined views of both for event-sequence data. Our studies underline that node-link and matrix views are suited to different analysis tasks. For the combined view, our studies show that there is a perceptually complementary effect in terms of improved accuracy for some tasks, but that there is a cost in terms of longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants strongly favour one of the two views, after trying both in the training phase.