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 through an expert interview.

翻译：多变量网络在真实世界的数据驱动应用中普遍存在。揭示并理解多变量网络中的目标关系并非易事。本文提出一种面向多变量网络的可视分析工作流，旨在提取网络不同结构与语义特征之间的关联（例如：哪些属性组合与社交网络密度高度相关？）。该工作流包含以下阶段：基于神经网络的学习阶段，通过选定的输入和输出属性对数据进行分类；降维与优化阶段，生成简化后的结果集供分析；最终由用户通过交互式可视化界面进行解释阶段。我们设计的核心是复合变量构建步骤，该步骤将神经网络获得的非线性特征重塑为直观可解释的线性特征。通过多个基于社交媒体使用数据的网络案例研究，我们展示了该工作流的能力，并基于专家访谈对其进行了评估。