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.

翻译：多变量网络在现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感兴趣的关联并非易事。本文提出了一种用于研究多变量网络的可视化分析工作流，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？）。该工作流包含基于神经网络的学习阶段（根据选定的输入与输出属性对数据进行分类）、降维与优化阶段（生成简化的结果集以供检验），以及最终用户通过交互式可视化界面进行的解释阶段。我们设计的一个关键部分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构为易于解释的线性特征。我们通过社交媒体使用衍生的网络进行多个案例研究，展示了该工作流的能力，并借助专家的定性反馈对工作流进行了评估。