Multi2‐Vis: Guiding Interactive Exploration Across Temporal Scales in Dynamic Graphs

Abstract 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.