Towards Visualizing Big Data with Large-Scale Edge Constraint Graph Drawing

Abstract Visualization plays an important role in enabling understanding of big data. Graphs are crucial tools for visual analytics of big data networks such as social, biological, traffic and security networks. Graph drawing has been intensively researched to enhance aesthetic features (i.e., layouts, symmetry, cross-free edges). Early physic-inspired techniques have focused on synthetic abstract graphs whose weights/distances of the edges are often ignored or assumed equal. Although recent approaches have been extended to sophisticated realistic networks, most are not designed to address very large-scale weighted graphs, which are important for visual analyses. The difficulty lies in the fact that the drawing process, governed by these physical properties, oscillates in large graphs and conflicts with specified distances leading to poor visual results. Our research attempts to alleviate these obstacles. This paper presents a simple graph visualization technique that aims to efficiently draw aesthetically pleasing large-scale straight-line weighted edge graphs. Our approach uses relevant physic-inspired techniques to promote aesthetic graphs and proposes a weak constraint-based approach to handle large-scale computing and competing goals to satisfy both weight requirements and aesthetic properties. The paper describes the approach along with experiments on both synthetic and real large-scale weighted graphs including that of over 10,000 nodes and comparisons with state-of-the-art approaches. The results obtained show enhanced and promising outcomes toward a general-purpose graph drawing technique for both big synthetic and real network data analytics.