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.

可视化在理解大数据方面起着重要作用。图形是用于大数据网络（例如社交，生物，流量和安全网络）的可视化分析的重要工具。为了增强美学特征（例如，布局，对称性，无交叉边缘），已经对图形绘图进行了深入研究。早期的物理学启发技术专注于合成抽象图，其边缘的权重/距离通常被忽略或假定为相等。尽管最近的方法已扩展到复杂的现实网络，但大多数方法并未设计为处理非常大的加权图，这对于视觉分析非常重要。困难在于这样一个事实，即受这些物理属性控制的绘制过程会在大图中振荡，并与指定的距离发生冲突，从而导致视觉效果不佳。我们的研究试图减轻这些障碍。本文提出了一种简单的图形可视化技术，旨在有效地绘制美观的大规模直线加权边缘图。我们的方法采用了相关的物理启发技术来推广美学图，并提出了基于弱约束的方法来处理大规模计算和竞争目标，以满足重量要求和美学特性。本文介绍了该方法以及在合成和真实大规模加权图上的实验，包括超过10,000个节点的图，以及与最新方法的比较。所获得的结果表明，针对大型综合和实际网络数据分析的通用图形绘制技术具有增强而有希望的结果。