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：多元网络常见于现实世界的数据驱动应用中。发现并理解多元网络中感兴趣的关系并非易事。本文提出了一种用于研究多元网络的可视化分析工作流程，旨在提取网络不同结构特征与语义特征之间的关联（例如，哪些属性组合与社会网络的密度密切相关？）。该工作流程包含以下阶段：基于神经网络的学习阶段（根据选定的输入和输出属性对数据进行分类）、降维与优化阶段（生成简化的结果集以供检查），以及最终由用户通过交互式可视化界面执行的解释阶段。我们设计的关键环节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易于解释的线性特征。通过基于社交媒体使用数据的多个案例研究，我们展示了该工作流程的能力，并通过专家访谈对其进行了评估。