Sujia Zhu

Sujia Zhu, Yue Shen, Zihao Zhu et al.

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 sequences data mainly focus on individual causal discovery, which is incapable of exploiting combined causality. To fill the absence of combined causes discovery on temporal event sequence data,eliminating and recruiting principles are defined to balance the effectiveness and controllability on 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 our causality visualization is non-overlapping and non-intersecting. Diverse sorting strategies and aggregation layout are also embedded into our parallel-based, directed and weighted hypergraph for illustrating combined causality. Our developed combined causality visual analysis system can help users effectively explore combined causes as well as an individual cause. This interactive system supports multi-level causality exploration with diverse ordering strategies and a focus and context technique to help users obtain different levels of information abstraction. The usefulness and effectiveness of the system are further evaluated by conducting a pilot user study and two case studies on event sequence data.

Baofeng Chang, Sujia Zhu, Qi Jiang et al.

Dynamic graph visualization attracts researchers' concentration as it represents time-varying relationships between entities in multiple domains (e.g., social media analysis, academic cooperation analysis, team sports analysis). Integrating visual analytic methods is consequential in presenting, comparing, and reviewing dynamic graphs. Even though dynamic graph visualization is developed for many years, how to effectively visualize large-scale and time-intensive dynamic graph data with subtle changes is still challenging for researchers. To provide an effective analysis method for this type of dynamic graph data, we propose a snapshot generation algorithm involving Human-In-Loop to help users divide the dynamic graphs into multi-granularity and hierarchical snapshots for further analysis. In addition, we design a visual analysis prototype system (DGSVis) to assist users in accessing the dynamic graph insights effectively. DGSVis integrates a graphical operation interface to help users generate snapshots visually and interactively. It is equipped with the overview and details for visualizing hierarchical snapshots of the dynamic graph data. To illustrate the usability and efficiency of our proposed methods for this type of dynamic graph data, we introduce two case studies based on basketball player networks in a competition. In addition, we conduct an evaluation and receive exciting feedback from experienced visualization experts.