Net-Ray: Visualizing and Mining Billion-Scale Graphs

U Kang, Jay Yoon Lee, Danai Koutra, Christos Faloutsos

Abstract: How can we visualize billion-scale graphs? How to spot outliers in such graphs quickly? Visualizing graphs is the most direct way of understanding 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 visualizationbased mining on billion-scale graphs. Net-Ray visualizes graphs using the spy plot (adjacency matrix patterns), distribution plot, and correlation plot which involve careful node ordering and scaling. In addition, Net-Ray efficiently summarizes 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 billions of nodes and edges efficiently and effectively. Specifically, among the various 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 whofollows- 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.