Fuchs, JohannesDennig, Frederik L.Heinle, Maria-ViktoriaKeim, Daniel A.Bartolomeo, Sara DiAigner, WolfgangArchambault, DanielBujack, Roxana2024-05-212024-05-2120241467-8659https://doi.org/10.1111/cgf.15079https://diglib.eg.org/handle/10.1111/cgf15079The visual analysis of multivariate network data is a common yet difficult task in many domains. The major challenge is to visualize the network's topology and additional attributes for entities and their connections. Although node-link diagrams and adjacency matrices are widespread, they have inherent limitations. Node-link diagrams struggle to scale effectively, while adjacency matrices can fail to represent network topologies clearly. In this paper, we delve into the design space of BioFabric, which aligns entities along rows and relationships along columns, providing a way to encapsulate multiple attributes for both. We explore how we can leverage the unique opportunities offered by BioFabric's design space to visualize multivariate network data - focusing on three main categories: juxtaposed visualizations, embedded on-node and on-edge encoding, and transformed node and edge encoding. We complement our exploration with a quantitative assessment comparing BioFabric to adjacency matrices. We postulate that the expansive design possibilities introduced in BioFabric network visualization have the potential for the visualization of multivariate data, and we advocate for further evaluation of the associated design space. Our supplemental material is available on osf.io.CCS Concepts: Human-centered computing → Empirical studies in visualization; Graph drawingsHuman centered computing → Empirical studies in visualizationGraph drawings10.1111/cgf.1507912 pages