Preserving Minority Structures in Graph Sampling

TL;DR

This paper introduces a novel graph sampling method called MCGS that effectively preserves rare and small minority structures, which are often overlooked by existing algorithms, thereby improving graph analysis and visualization.

Contribution

The paper proposes MCGS, a new graph sampling approach that uses triangle and cut-point algorithms along with importance criteria to better preserve minority structures.

Findings

MCGS outperforms existing sampling algorithms in minority structure preservation.

Experimental results show improved accuracy in maintaining minority structures.

Case studies demonstrate the practical effectiveness of MCGS.

Abstract

Sampling is a widely used graph reduction technique to accelerate graph computations and simplify graph visualizations. By comprehensively analyzing the literature on graph sampling, we assume that existing algorithms cannot effectively preserve minority structures that are rare and small in a graph but are very important in graph analysis. In this work, we initially conduct a pilot user study to investigate representative minority structures that are most appealing to human viewers. We then perform an experimental study to evaluate the performance of existing graph sampling algorithms regarding minority structure preservation. Results confirm our assumption and suggest key points for designing a new graph sampling approach named mino-centric graph sampling (MCGS). In this approach, a triangle-based algorithm and a cut-point-based algorithm are proposed to efficiently identify minority structures. A set of importance assessment criteria are designed to guide the preservation of important minority structures. Three optimization objectives are introduced into a greedy strategy to balance the preservation between minority and majority structures and suppress the generation of new minority structures. A series of experiments and case studies are conducted to evaluate the effectiveness of the proposed MCGS.