Visual Evaluation for Attribute Differences in Graph Sampling

With the increasing scale and complexity of graph data, graph sampling has become a crucial dimensionality reduction technique, while the evaluation of its effectiveness has also garnered significant attention. However, traditional sampling evaluation methods primarily focus on preserving topological structures while neglecting the integrity of node attributes. To address this, we introduce an attribute-aware evaluation framework for assessing graph sampling differences. First, a novel taxonomy is proposed to categorize sampling differences into two major types: Attribute-Isolation and Attribute-Structure. The former is further divided into Attribute-Distribution and Attribute-Correlation, while the latter includes three subcategories: Attribute-Node, Attribute-Path, and Attribute-Community. Based on this, we design a set of computable metrics to quantify each type of attribute-related differences. Furthermore, we develop an interactive visualization system that integrates multiview visual modules to visualize sampling impacts on attributes and structural correlations. Case studies and quantitative evaluations demonstrate the effectiveness of our method in characterizing the impact of different sampling strategies on attribute information and aiding users in network analysis and strategy selection.