ZipLine：基于谓词逻辑的多元图可视化分析
ZipLine: Visual Analysis of Multivariate Graphs with Predicate Logic
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中文总结 AI 辅助
研究多元图可视化分析问题，提出ZipLine系统，引入谓词语言及学习算法，通过连接拓扑和属性空间支持综合分析，经案例研究证明能实现跨两空间的统一推理及多元图可视化分析。
中文摘要 AI 辅助
多元图结合了由节点和边定义的拓扑结构以及与每个节点相关的属性数据这两个不同的数据视角。现有系统通常一次只强调对一个空间的分析，导致依赖两者交互的探索、分析和模式发现仍很困难。本文提出ZipLine系统，通过连接拓扑和属性空间来支持多元图的综合分析。它引入谓词语言，使分析师能用统一形式表达涉及拓扑、节点属性和邻域关系的模式。还提供谓词学习算法，将分析师在拓扑和属性视图上的交互映射到谓词语言中，支持通过协调推理进行迭代分析。通过三个案例研究证明，ZipLine能通过跨拓扑和属性的统一推理实现多元图的可视化分析。
英文摘要
Multivariate graphs unite two distinct data perspectives: a topological structure defined by nodes and edges, and attribute data associated with each node. Analyzing such graphs therefore requires reasoning across two complementary spaces. However, existing systems typically emphasize the analysis of one space at a time, focusing either on topology or on attributes. As a result, exploration, analysis, and pattern discovery that depend on their interaction remain difficult. In this paper, we present ZipLine, a system designed to support integrative analysis of multivariate graphs by bridging both topology and attribute spaces. ZipLine introduces a predicate language that enables analysts to express patterns involving topology, node attributes, and neighborhood relations with a unified formalism. The system further provides a predicate-learning algorithm that maps analyst interactions across both topology (e.g., subgraph selection) and attribute views (e.g., value brushing), into the predicate language, enabling learned expressions that bridge the two spaces. This integrative approach supports iterative analysis by enabling analysts to refine patterns through coordinated reasoning over topology and attributes. We demonstrate ZipLine through three case studies in energy infrastructure, cybersecurity, and drug discovery analysis. The results show that ZipLine enables expressive multivariate graph analysis through unified reasoning across topology and attributes.
