DiffLens: A Visualization System to Explore Local Differences in Graph Sampling

2026-07-13

AI Summary

This work addresses the lack of systematic approaches for quantifying and exploring local discrepancies between sampled graphs and their original counterparts at the node, edge, and structural levels—a gap that hinders effective evaluation and selection of graph sampling strategies. To bridge this gap, the authors propose three general quantitative metrics—neighborhood-, path-, and structure-based—to measure local fidelity. They further introduce DiffLens, an interactive visualization system that, for the first time, incorporates lens-based views tailored to these three types of differences, enabling users to focus on regions of interest. Case studies on real-world network datasets and user experiments demonstrate the framework’s effectiveness and practicality in supporting intuitive comparison of sampling outcomes and enhancing understanding of localized structural variations.

Abstract

Graph sampling techniques have been widely used to simplify network computation and visualization, which also results in inevitable differences between the sampled networks and the original networks in terms of nodes, edges and structures. Investigating such differences can inform graph sampling technique users of the pros and cons of different techniques and select the appropriate one, and can also help graph sampling developers evaluate their own technique. However, there are still no systematic ways to achieve such a goal. This paper fills this research gap by first proposing systematic and generic quantitative measures to quantify three categories of graph differences (i.e., neighbor-based, path-based, and structure-based). Built upon this, we further propose DiffLens, a novel visualization system to help graph sampling developers and users intuitively explore local differences at different regions of their interest within a sampled graph, where three new lens-based visual designs are presented to display the neighbor-based, path-based, and structure-based differences respectively. We conducted two case studies and a user study using real-world network datasets to evaluate DiffLens. The results confirmed its effectiveness and usability in helping users explore local differences and compare different graph sampling strategies.

Problem

• graph sampling
• local differences
• network visualization
• graph comparison
• sampling evaluation

Innovation

• graph sampling
• difference visualization
• lens-based visualization
• quantitative metrics
• network comparison