图抽样中少数结构的保持
Preserving Minority Structures in Graph Sampling
摘要：
采样是一种广泛使用的图形简化技术，用于加速图形计算和简化图形可视化。通过对图抽样相关文献的综合分析，我们假设现有的算法不能有效地保留图中罕见的、小的、但在图分析中非常重要的少数结构。在这项工作中，我们首先进行了一项试点用户研究，以调查最吸引人类观众的代表性少数民族结构。然后，我们进行了一项实验研究，以评估现有的图抽样算法的性能少数结构保护。结果证实了我们的假设，并建议设计一个新的图抽样方法命名为mino-centric图抽样（MCGS）的关键点。在该方法中，提出了一种基于三角形的算法和一种基于切割点的算法，以有效地识别少数结构。设计了一套重要性评估标准，以指导重要的少数民族建筑的保护。在贪婪策略中引入三个优化目标，以平衡少数结构和多数结构之间的保留，并抑制新的少数结构的产生。一系列的实验和案例研究进行了评估建议MCGS的有效性。
英文摘要：
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.
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