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Multivariate networks are commonly found in real-world data-drivenapplications. Uncovering and understanding the relations of interest inmultivariate networks is not a trivial task. This paper presents a visualanalytics workflow for studying multivariate networks to extract associationsbetween different structural and semantic characteristics of the networks(e.g., what are the combinations of attributes largely relating to the densityof a social network?). The workflow consists of a neural-network-based learningphase to classify the data based on the chosen input and output attributes, adimensionality reduction and optimization phase to produce a simplified set ofresults for examination, and finally an interpreting phase conducted by theuser through an interactive visualization interface. A key part of our designis a composite variable construction step that remodels nonlinear featuresobtained by neural networks into linear features that are intuitive tointerpret. We demonstrate the capabilities of this workflow with multiple casestudies on networks derived from social media usage and also evaluate theworkflow through an expert interview.

Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction