VAC2: Visual Analysis of Combined Causality in Event Sequences
Sujia Zhu, Guodao Sun, Yue Shen, Zihao Zhu, Wang Xia, Baofeng Chang, Jingwei Tang, Ronghua Liang · IEEE Transactions on Visualization and Computer Graphics · 2024
Identifying causality behind complex systems plays a significant role in different domains, such as decision-making, policy implementations, and management recommendations. However, existing causality studies on temporal event sequence data mainly focus on individual causal discovery, which is incapable of capturing combined causality. To address the gap in combined causality discovery on temporal event sequence data, eliminating and recruiting principles are defined to balance the effectiveness and controllability of cause combinations. We also leverage the Granger causality algorithm based on the Reactive point processes to describe impelling or inhibiting behavior patterns among entities. In addition, we design an informative and aesthetic visual metaphor of "electrocircuit" to encode aggregated causality for ensuring that our causality visualization exhibits no node-overlap, no edge-intersection, and no link-ambiguity. Aggregation layout, diverse sorting strategies, and smooth interactions are also integrated into our directed, weighted, and parallel-based hypergraph for illustrating combined causality. Our developed combined causality visual analysis system, namely VAC$^{2}$2, can help users effectively explore combined causes as well as individual causes. This interactive system supports multi-level causality exploration with diverse ordering strategies and a focus+context technique to help users obtain different levels of information abstraction. The usefulness and effectiveness of our work are further evaluated by conducting two case studies and a controlled user study on event sequence data.