  Multivariate networks are commonly found in real-world data-driven
applications. Uncovering and understanding the relations of interest in
multivariate networks is not a trivial task. This paper presents a visual
analytics workflow for studying multivariate networks to extract associations
between different structural and semantic characteristics of the networks
(e.g., what are the combinations of attributes largely relating to the density
of a social network?). The workflow consists of a neural-network-based learning
phase to classify the data based on the chosen input and output attributes, a
dimensionality reduction and optimization phase to produce a simplified set of
results for examination, and finally an interpreting phase conducted by the
user through an interactive visualization interface. A key part of our design
is a composite variable construction step that remodels nonlinear features
obtained by neural networks into linear features that are intuitive to
interpret. We demonstrate the capabilities of this workflow with multiple case
studies on networks derived from social media usage and also evaluate the
workflow with qualitative feedback from experts.
