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Received:January 26, 2015 Revised:March 19, 2015
Received:January 26, 2015 Revised:March 19, 2015
中文摘要: 复杂网络的可视化是复杂网络研究中的重要手段.随着Web2.0时代和大数据时代的来临,作为研究对象的复杂网络的规模越来越大,这对复杂网络可视化布局算法的布局效果和运算速度提出了新的挑战.本文针对复杂网络布局的力导引算法,从布局效果和算法效率两方面对该算法进行了改进和实现.布局效果方面,利用复杂网络中的关节点,对网络数据进行抽象合并,从而实现分层次的网络布局显示.算法效率方面,针对压缩后的网络采用具有强大浮点运算能力的GPU进行计算,对力导引算法需要斥力计算、引力计算和坐标更新三个部分均实现了基于GPU的并行计算,大大提高了计算效率.
Abstract:One of the ways to study complex networks is making them visualized. With the advent of Web2.0 era and the big data era, the scale of the complex networks is becoming larger. That brings the new challenge for the visualization of complex networks on the layout effect and speed. This paper aims at improving the FDA(Force-Directed Algorithm) from this two aspects. On the layout effect, the articulation points of the complex networks are used to compact the networks, which achieves showing the layout result hierarchically. On the speed of the algorithm, repulsive force and attraction are computed on GPU, and the operation of updating the coordinates of vertex is also implemented on GPU. That improves the efficiency of the algorithm greatly.
keywords: complex network visualization layout algorithm force-directed algorithm articulation points
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基金项目:北京高等学校青年英才计划(YETP0506)
引用文本：
李甜甜,卢罡,许南山,郭俊霞.基于GPU的大尺度网络布局显示.计算机系统应用,2015,24(12):25-33
LI Tian-Tian,LU Gang,XU Nan-Shan,GUO Jun-Xia.Compressed Layout for Large Scale Networks Based on GPU.COMPUTER SYSTEMS APPLICATIONS,2015,24(12):25-33
李甜甜,卢罡,许南山,郭俊霞.基于GPU的大尺度网络布局显示.计算机系统应用,2015,24(12):25-33
LI Tian-Tian,LU Gang,XU Nan-Shan,GUO Jun-Xia.Compressed Layout for Large Scale Networks Based on GPU.COMPUTER SYSTEMS APPLICATIONS,2015,24(12):25-33
