A Survey on Graph Visualization

Abstract

Graph visualization helps users gain insight into data by turning the data elements and their internal relationships into graphs. Graph visualization leverages the human visual system to support knowledge discovery. It has been widely used in many applications, such as social networks, Internet communications, paper citations, and biochemical pathways. However, as data become very large nowadays, traditional graph visualization techniques may fail to reveal the pattern hidden in data. Massive data pose many challenges for graph visualization, such as visual clutter, layout, navigation, and evaluation criteria. In this survey, we first give a brief overview on graph representations and their layout algorithms. Then we will focus on various visualization techniques for massive graphs. Specifically, we will describe clutter reduction algorithms and user navigation techniques. After that, we introduce some common visualization tasks and discuss the evaluation schemes in typical applications. At the end of this survey, we point out some future research directions.