The growth of graph size has created new problems in graph visualization and graph analysis. To solve the problem, several graph sampling techniques have been proposed dedicated to obtaining a representative subgraph from a complex network. While prior research indicates that sampling on a large-scale graph is not an easy task, especially for topology-based sampling methods (e.g. breadth first sampling). Topology-based sampling methods can produce a more accurate subgraph than node sampling and edge sampling in preserving statistical graph properties. In this paper, we propose three types of distributed sampling algorithms and develop a sampling package on Spark. To evaluate the effectiveness of these distributed sampling techniques, we apply them to three graph datasets and compare them with traditional/non-distributed sampling approaches. The results show that (1) our distributed sampling approaches are as reliable as the non-distributed sampling techniques, and (2) they are a great improvement in sampling efficiency, especially for topology-based sampling. In addition, (3) the distributed architecture of these algorithms causes them to have horizontal scalability.
Storytelling animation has a great potential to be widely adopted by domain scientists for exploring trends in scientific simulations. However, due to the dynamic nature and generation methods of animations, serious concerns have been raised regarding their effectiveness for analytical tasks. This has led to interactive techniques often being favored over animations, as they provide the user with complete control over the visualization. This trend in scientific visualization design has not yet considered newer algorithmic animation generation methods that are driven by the automatic analysis of data features and storytelling techniques. In this work, the authors performed an experiment which compares feature-driven storytelling animations to common interactive visualization techniques for time-varying scientific simulations. They discuss the design of the experiment, including tasks for storm-surge analysis that are representative of common scientific visualization projects. Their results illustrate the relative advantages of both feature-driven storytelling animations and interactive visualizations, which may provide useful design guidelines for future storytelling and scientific visualization techniques. © 2016 Society for Imaging Science and Technology.
We evaluate a dozen prevailing graph-sampling techniques with an ultimate goal to better visualize and understand big and complex graphs that exhibit different properties and structures. The evaluation uses eight benchmark datasets with four different graph types collected from Stanford Network Analysis Platform and NetworkX to give a comprehensive comparison of various types of graphs. The study provides a practical guideline for visualizing big graphs of different sizes and structures. The paper discusses results and important observations from the study.