Qiushi Xia

Selecting appropriate temporal intervals for analyzing dynamic graphs is a critical but non‐trivial task. Poorly chosen intervals can obscure key structural changes and lead to flawed interpretations, while existing tools often lack effective guidance, forcing users into a cycle of tedious manual adjustments and trial‐and‐error. We present Multi2‐Vis, a visual analytics system that reframes temporal segmentation as a human‐in‐the‐loop interactive refinement process. Multi2‐Vis employs an Evaluate‐Recommend‐Refine loop: it uses structure‐aware metrics to automatically identify suboptimal segments and then presents optimized alternatives in a multi‐scale visual interface, guiding users toward informed decisions. Through two case studies, a quantitative experiment and a controlled user study, we demonstrate that our guided workflow significantly improves analysts' analytical efficiency and conclusion quality compared to traditional methods. By transforming temporal segmentation from a rigid prerequisite into a flexible, interactive dialogue, Multi2‐Vis provides a more interpretable solution for dynamic graph analysis.

Bus route planning is a complex application problem within the transportation domain, aiming to identify the best route among numerous candidate solutions. Despite existing research significantly reducing the exploration space of solutions, planners still face challenges in further exploring optimal route planning solutions. Specifically, the diversity of route attributes increases the complexity of determining their impact, such as the variety and quantity of reachable points of interest. Therefore, we present BRPVis, an interactive visual analytics system designed to assist bus route planners in exploring optimal solutions through multi-level visualization and rich interaction design. Furthermore, we propose a human-machine collaborative multicriteria decision-making method, which quantitatively analyzes the weights of route attributes while incorporating interactive feedback mechanisms to support personalized route exploration. Based on exploration using real-world traffic datasets, three case studies conducted with domain experts demonstrate that BRPVis effectively provides decision support for bus route planning tasks.