ZipLine: Visual Analysis of Multivariate Graphs with Predicate Logic

2026-07-15

AI Summary

Existing graph analysis systems struggle to effectively integrate topological structure with node attributes, limiting the discovery of patterns driven by their interaction. This work proposes ZipLine, a novel system that, for the first time, unifies predicate logic to express topology, node attributes, and neighborhood relationships within a single formalism. ZipLine introduces an interaction-driven predicate learning algorithm that enables cross-space collaborative reasoning and iterative analysis. By integrating coordinated views, subgraph selection, and attribute brushing techniques, the system facilitates expressive and efficient exploration of complex patterns in multivariate graphs. Empirical evaluation across three real-world domains—energy infrastructure, cybersecurity, and drug discovery—demonstrates ZipLine’s effectiveness in significantly enhancing the expressiveness and discoverability of intricate graph patterns.

Abstract

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.

Problem

• multivariate graphs
• topology
• node attributes
• integrated analysis
• visual analytics

Innovation

• predicate logic
• multivariate graphs
• integrated analysis
• topology-attribute bridging
• interactive learning