Bridging Network Science And Vision Science: Mapping Perceptual Mechanisms To Network Visualization Tasks

Network visualizations are understudied in graphical perception. As a result, most network visualization designs still largely rely on designer intuition and algorithm optimizations rather than being guided by knowledge of human perception. The lack of perceptual understanding of network visualizations also limits the generalizability of past empirical evaluations, given their focus on performance over causal interpretation. To bridge this gap between perception and network visualization, we introduce a framework highlighting five key perceptual mechanisms used in node-link diagrams and adjacency matrices: attention, visual search, perceptual organization, ensemble coding, and object recognition. Our framework describes the role these perceptual mechanisms play in common network analytical tasks. We use the framework to revisit four past empirical investigations and outline future design experiments that can help produce more perceptually effective network visualizations. We anticipate this connection will afford translational understanding to guide more effective network visualization design and offer hypotheses for perception-aware network visualizations.

Sandra Bae, Kyle Cave, Carsten Gorg, Paul Rosen, Danielle Albers Szafir, and Cindy Xiong Bearfield. Bridging Network Science And Vision Science: Mapping Perceptual Mechanisms To Network Visualization Tasks. To appear in IEEE Transactions on Computer Graphics and Visualization, 2025.

@article{bae2025network,
  title = {Bridging Network Science and Vision Science: Mapping Perceptual Mechanisms to
    Network Visualization Tasks},
  author = {Bae, Sandra and Cave, Kyle and Gorg, Carsten and Rosen, Paul and Szafir,
    Danielle Albers and Bearfield, Cindy Xiong},
  journal = {To appear in IEEE Transactions on Computer Graphics and Visualization},
  year = {2025},
  abstract = {Network visualizations are understudied in graphical perception. As a
    result, most network visualization designs still largely rely on designer intuition and
    algorithm optimizations rather than being guided by knowledge of human perception. The
    lack of perceptual understanding of network visualizations also limits the
    generalizability of past empirical evaluations, given their focus on performance over
    causal interpretation. To bridge this gap between perception and network visualization,
    we introduce a framework highlighting five key perceptual mechanisms used in node-link
    diagrams and adjacency matrices: attention, visual search, perceptual organization,
    ensemble coding, and object recognition. Our framework describes the role these
    perceptual mechanisms play in common network analytical tasks. We use the framework to
    revisit four past empirical investigations and outline future design experiments that
    can help produce more perceptually effective network visualizations. We anticipate this
    connection will afford translational understanding to guide more effective network
    visualization design and offer hypotheses for perception-aware network visualizations.}
}