Netray: Visualizing and mining billion-scale graphs

Abstract

Abstract. How can we visualize billion-scale graphs? How to spot outliers in such graphs quickly? Visualizing graphs is the most direct way of understand-ing them; however, billion-scale graphs are very difficult to visualize since the amount of information overflows the resolution of a typical screen. In this paper we propose NET-RAY, an open-source package for visualization-based mining on billion-scale graphs. NET-RAY visualizes graphs using the spy plot (adjacency matrix patterns), distribution plot, and correlation plot which in-volve careful node ordering and scaling. In addition, NET-RAY efficiently sum-marizes scatter clusters of graphs in a way that finds outliers automatically, and makes it easy to interpret them visually. Extensive experiments show that NET-RAY handles very large graphs with bil-lions of nodes and edges efficiently and effectively. Specifically, among the var-ious datasets that we study, we visualize in multiple ways the YahooWeb graph which spans 1.4 billion webpages and 6.6 billion links, and the Twitter who-follows-whom graph, which consists of 62.5 million users and 1.8 billion edges. We report interesting clusters and outliers spotted and summarized by NET-RAY.