基于粒子群优化算法的社交网络可视化

为了使用户快捷、清晰地发现及研究微博用户之间的关系,提出基于粒子群优化(PSO)算法的微博数据可视化方法.根据用户在微博中的影响力将用户分为n层,以此来表示用户在网络中对信息的传播影响力的等级.基于数据的关联关系对数据进行子群划分；基于粒子群优化算法,设计目标函数,使粒子群优化算法适应社交网络的布局要求.为了进一步增强可视化效果,降低视觉复杂度,采用曲线代替直线,应用传输函数设置不透明度以及交互的可视化技术.实验结果表明,该方法可以形成清晰的可视化结果,以便更好地分析微博用户之间的关系.

Visualization of social network based on particle swarm optimization

A visualization method based on particle swarm optimization (PSO) for microblogging data was proposed in order to assist users to reveal and analyze the relationship among microblogging users more clearly and quickly. According to their influence, users were divided into n layers in order to represent how much the user can influence the dissemination of information in the network. Users were divided into subgroups based on their focus relationship; the objective function was designed based on the PSO algorithm in order to meet the layout requirements of social networks. Straight lines were replaced with curve lines in order to further enhance the visualization results and reduce the visual complexity. Transfer function and interaction techniques were employed. Experimental results showed that the proposed method formed a clear visual result and provided a better analysis of relationship among the microblogging users.