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Open Access A Visual Evaluation Study of Graph Sampling Techniques

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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.
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Keywords: BIG GRAPHS; GRAPH DRAWING; GRAPH PROPERTIES; GRAPH SAMPLING; VISUAL ANALYTICS; VISUALIZATION

Document Type: Research Article

Publication date: 2017-01-29

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