论文标题
在线强制定向算法，用于可视化动态图
Online force-directed algorithms for visualization of dynamic graphs
论文作者
论文摘要
实力定向（FD）算法可用于探索社交网络中的关系，可视化货币市场和分析交易网络。但是，FD算法主要设计用于可视化静态图，在整个计算过程中，网络的拓扑保持恒定。与静态图相反，随着时间的流逝，可以添加或删除动态图中的节点和边缘。在这些情况下，现有的FD算法不能很好地扩展，因为拓扑的任何变化都会触发这些算法以完全重新启动整个计算。为了减轻这个问题，我们提出了五种在线FD算法的设计和实施，以使动态图可视化，同时维护其本地力量模型。本文开发的在线FD算法能够重复使用现有FD算法的力模型，而无需进行重大修改。为了评估所提出方法的有效性，将在线FD算法与可视化动态图的静态FD算法进行了比较。实验结果表明，在评估的五种算法中，在线FD算法实现了边缘交叉数的最佳数量和用于可视化动态图的边缘长度的标准偏差。
Force-directed (FD) algorithms can be used to explore relationships in social networks, visualize money markets, and analyze transaction networks. However, FD algorithms are mainly designed for visualizing static graphs in which the topology of the networks remains constant throughout the calculation. In contrast to static graphs, nodes and edges in dynamic graphs can be added or removed as time progresses. In these situations, existing FD algorithms do not scale well, since any changes in the topology will trigger these algorithms to completely restart the entire computation. To alleviate this problem, we propose a design and implementation of five online FD algorithms to visualize dynamic graphs while maintaining their native force models. The online FD algorithms developed in this paper are able to reuse the force models of existing FD algorithms without significant modifications. To evaluate the effectiveness of the proposed approach, online FD algorithms are compared against static FD algorithms for visualizing dynamic graphs. Experimental results show that among the five algorithms evaluated, the online FD algorithm achieves the best number of edge crossings and the standard deviation of edge lengths for visualizing dynamic graphs.
