Issue No. 01 - Jan. (2017 vol. 23)
Yanhong Wu , Hong Kong University of Science and Technology
Nan Cao , New York University, Shanghai
Daniel Archambault , Swansea University
Qiaomu Shen , Hong Kong University of Science and Technology
Huamin Qu , Hong Kong University of Science and Technology
Weiwei Cui , Microsoft Research Asia
Graph sampling is frequently used to address scalability issues when analyzing large graphs. Many algorithms have been proposed to sample graphs, and the performance of these algorithms has been quantified through metrics based on graph structural properties preserved by the sampling: degree distribution, clustering coefficient, and others. However, a perspective that is missing is the impact of these sampling strategies on the resultant visualizations. In this paper, we present the results of three user studies that investigate how sampling strategies influence node-link visualizations of graphs. In particular, five sampling strategies widely used in the graph mining literature are tested to determine how well they preserve visual features in node-link diagrams. Our results show that depending on the sampling strategy used different visual features are preserved. These results provide a complimentary view to metric evaluations conducted in the graph mining literature and provide an impetus to conduct future visualization studies.
Visualization, Measurement, Data visualization, Data mining, Fires, Scalability, Clustering algorithms
Y. Wu, N. Cao, D. Archambault, Q. Shen, H. Qu and W. Cui, "Evaluation of Graph Sampling: A Visualization Perspective," in IEEE Transactions on Visualization & Computer Graphics, vol. 23, no. 1, pp. 401-410, 2017.