Honorable Mention

Preserving Minority Structures in Graph Sampling

Ying Zhao, Haojin Jiang, Qi'an Chen, Yaqi Qin, Huixuan Xie, Yitao Wu, Shixia Liu, Zhiguang Zhou, Jiazhi Xia, Fangfang Zhou

View presentation: 2020-10-30T14:00:00Z GMT-0600 Change your timezone on the schedule page
Exemplar figure
Illustration of four representative types of minority structures in a toy case graph (a) and seven graph samples obtained by RDN (b), TIES (c), BF(d), FF (e), RW (f), SST (g), and our proposed MCGS (h), respectively, with a sampling rate of 50%.

Graph sampling, graph visualization, node-link diagram


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.