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Proxy Graph: Visual Quality Metrics of Big Graph Sampling

• Quan Nguyen quan.nguyen (at) sydney.edu.au
  Sydney University
• Seok-Hee Hong seokhee.hong (at) sydney.edu.au
  Sydney University
• Peter Eades peter.eades (at) sydney.edu.au
  Sydney University
• Amyra Meidiana amei2916 (at) uni.sydney.edu.au
  Sydney University

Abstract

Data sampling has been extensively studied for large scale graph mining. \ Many analyses and tasks become more efficient when performed on graph samples of much smaller size. The use of \\emph{proxy} objects is common in software engineering for analysis and interaction with heavy objects or systems. \ In this paper, we coin the term 'proxy graph' and empirically investigate how well a proxy graph visualization can represent a big graph. \ \ Our investigation focuses on proxy graphs obtained by \\emph{sampling}; this is one of the most common proxy approaches. Despite the plethora of data sampling studies, this is the first evaluation of sampling in the context of graph visualization. \ \ \ For an objective evaluation, we propose a new family of quality \\emph{metrics} for visual quality of proxy graphs. Our experiments cover popular sampling techniques. Our experimental results lead to guidelines for using sampling-based proxy graphs in visualization.