Please wait a minute...
J4  2013, Vol. 47 Issue (1): 37-43    DOI: 10.3785/j.issn.1008-973X.2013.01.006
计算机技术﹑电信技术     
基于粒子群优化算法的社交网络可视化
刘芳, 孙芸, 杨庚, 林海
浙江大学 CAD&CG国家重点实验室,浙江 杭州 310058
Visualization of social network based on particle swarm optimization
LIU Fang, SUN Yun, YANG Geng, LIN Hai
State Key Laboratory of CAD & CG, Zhejiang University, Hangzhou 310058, China
 全文: PDF 
摘要:

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.

关键词:  微博粒子群优化(PSO)可视化分析子群社交网络    
Abstract:

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.

Key words: microblogging    particle swarm optimization (PSO)    visual analysis    subgroup    social network
出版日期: 2013-03-05
:  TP 391  
基金资助:

国家自然科学基金资助项目(60873122, 60903133)

通讯作者: 林海,男,教授,博导.     E-mail: lin@cad.zju.edu.cn
作者简介: 刘芳(1976-),女,博士生,从事可视化的研究.E-mail:liufang@cad.zju.edu.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  

引用本文:

刘芳, 孙芸, 杨庚, 林海. 基于粒子群优化算法的社交网络可视化[J]. J4, 2013, 47(1): 37-43.

LIU Fang, SUN Yun, YANG Geng, LIN Hai. Visualization of social network based on particle swarm optimization. J4, 2013, 47(1): 37-43.

链接本文:

http://www.zjujournals.com/xueshu/eng/CN/10.3785/j.issn.1008-973X.2013.01.006        http://www.zjujournals.com/xueshu/eng/CN/Y2013/V47/I1/37

[1] WATTS D J, STROGATZ S H. Collective dynamics of ‘small-world-networks [J]. Nature, 1998, 393(6): 440-442.
[2] BARABASI A L, BONABEAU E. Scale-free networks [J]. Scientific American, 2003, 288(5): 50-59.
[3] LONG B,ZHANG M,WU X,et al. Spectral clustering for multitype relational data [C]∥ Proceedings of the 23rd International Conference on Machine Learning. New York: ACM,2006: 585-592.
[4] LI T, ANAND S S. DIVA a variancebased clustering approach for multi-type relational data [C]∥ Proceedings of the 16th ACM Conference on Information and Knowledge Management CIKM. New York: ACM, 2007: 147-156.
[5] YIN X X, HAN J W, PHILIP S Y. CrossClus: user-guided multi-relational clustering [J]. Data Mining Knowledge Discovery, 2007, 15: 321-348.
[6] CHENG Y, HUANG S B, LV T Y, et al. A hierarchical multi-relational clustering algorithm based on modal logic [C]∥ 2011 4th International Congress on Image and Signal Processing (CISP). Los Alamitos: IEEE, 2011: 2459-2463.
[7] HERMAN I, MELANCON G, MARSHALLl M S. Graph visualization and navigation in information visualization: a survey [J]. IEEE Transactions on Visualization and Computer Graphics, 2000, 6(1): 24-43.
[8] BEN M, EPPSTEIN D. Worst-case bounds for subadditive geometric graphs [C]∥ Proceedings of the 9th ACM Symposium on Computational Geometry. New York: ACM,1993: 183-188.
[9] NGUYEN Q V, HUANG M L. A space-optimized tree visualization [C]∥ IEEE Symposium on Information Visualization. Los Alamitos: IEEE, 2002:85-92.
[10] JOHNSON B, SHNEIDERMAN B. TreeMaps: a spacefilling approach to the visualization of hierarchical information [C]∥Proceedings of the Visualization 91. Los Alamitos: IEEE, 1991: 284-291.
[11] ROBERTSON G G, MACKINLAY J D, CARD S K. Cone trees: animated 3D visualizations of hierarchical information [C]∥ Proceedings of CHI 91 the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM, 1991: 189-194.
[12] SINDRE G, GULLA B, JOKSTAD H G. Onion graphs: aesthetics and layout [C]∥ Proceedings of IEEE Symposium on Visual Languages. Los Alamitos: IEEE, 1993: 287-291.
[13] EADES P. A heuristic for graph drawing [J]. Congressus Nutnerantiunt, 1984, 42(11): 149-160.
[14] KAMADA T, KAWAI S. An algorithm for drawing general undirected graphs [J]. Information Processing Letters (Elsevier), 1989, 31(1): 7-15.
[15] FRUCHTERMAN T M J, REINGLOD E M. Graph drawing by forcedirected placement [J]. Software-Practice and Experience (Wiley), 1991, 21(11):1129-1164.
[16] CHAN D S M, CHUA K S, LECKIE C, et al. Visualization of powerlaw network topologies [C]∥ Proceedings of the 11th IEEE International Conference on Networks. Los Alamitos: IEEE, 2003: 69-74.
[17] 吴鹏. 基于本体论的社会关系网络信息可视化研究[D]. 长沙:国防科学技术大学,2011.
WU Peng. Research on Ontology based information visualization of social network \
[D\]. Changsha: National University of Defense Technology, 2011.
[18] HUA J, HUANG M L, HUANG W D, et al. Forcedirected graph visualization with pre-positioning: improving convergence time and quality of layout [C]∥2012 16th International Conference on Information Visualization (IV). Los Alamitos: IEEE, 2012: 124-129.
[19] TAKAYUKI I, CHRIS M, MA K L, et al. A hybrid space-filling and force-directed layout method for visualizing multiplecategory graphs [C]∥Proceedings of IEEE Pacific Visualization 2009 Symposium. Los Alamitos: IEEE, 2009: 121-128.
[20] HENRY N, FEKETE J D. MatrixExplorer: a dual-representation system to explore social networks [J]. IEEE Transactions on Visualization and Computer Graphics, 2006, 12(5): 677-684.
[21] HENRY N, FEKETE J D, MCGUFFIN M J. NodeTrix: a hybrid visualization of social networks [J]. IEEE Transactions on Visualization and Computer Graphics, 2007, 13(6): 1302-1309.

[1] 张庆科, 孟祥旭, 张化祥, 杨波, 刘卫国. 基于随机维度划分与学习的粒子群优化算法[J]. 浙江大学学报(工学版), 2018, 52(2): 367-378.
[2] 陆源源, 王慧, 宋春跃. 考虑列车混行的运行调度一体化优化方法[J]. 浙江大学学报(工学版), 2018, 52(1): 106-116.
[3] 张亚楠, 陈德运, 王莹洁, 刘宇鹏. 基于增量图形模式匹配的动态冷启动推荐方法[J]. 浙江大学学报(工学版), 2017, 51(2): 408-415.
[4] 欧阳逸, 郭斌, 何萌, 於志文, 周兴社. 微博事件感知与脉络呈现系统[J]. 浙江大学学报(工学版), 2016, 50(6): 1176-1182.
[5] 张亚楠, 曲明成,刘宇鹏. 基于社交关系拓扑结构的冷启动推荐方法[J]. 浙江大学学报(工学版), 2016, 50(5): 1001-1008.
[6] 何海斌, 姚栋伟, 吴锋. 代用燃料焦炉气化学反应机理的搭建与优化[J]. 浙江大学学报(工学版), 2016, 50(10): 1841-1848.
[7] 刘湘琪,蒙臻,倪敬,朱泽飞. 三自由度液压伺服机械手轨迹优化[J]. 浙江大学学报(工学版), 2015, 49(9): 1776-1782.
[8] 尹娇妹, 赵昕玥, 张树有. 考虑Sobol缺陷敏感度的抗恶劣环境结构设计方法[J]. 浙江大学学报(工学版), 2015, 49(8): 1487-1494.
[9] 张震, 潘再平, 潘晓弘. 骨干粒子群算法两种不同实现的优化特性[J]. 浙江大学学报(工学版), 2015, 49(7): 1350-1357.
[10] 黄发明, 殷坤龙, 张桂荣, 唐志政, 张俊. 多变量PSO-SVM模型预测滑坡地下水位[J]. 浙江大学学报(工学版), 2015, 49(6): 1193-1200.
[11] 刘磊,杨鹏,刘作军. 基于多源信息和粒子群优化算法的下肢运动模式识别[J]. 浙江大学学报(工学版), 2015, 49(3): 439-447.
[12] 董如良, 杨强, 颜文俊. 多智能体协同寻优的主动配网动态拓扑重构[J]. 浙江大学学报(工学版), 2015, 49(10): 1982-1989.
[13] 陈特欢, 徐巍华, 许超, 谢磊. 基于PSO的管道泄漏模型反问题求解及敏感性分析[J]. 浙江大学学报(工学版), 2014, 48(10): 1850-1855.
[14] 刘业峰,徐冠群,潘全科,柴天佑. 磁性材料成型烧结生产调度优化方法及应用[J]. J4, 2013, 47(9): 1517-1523.
[15] 夏美梦,关富玲. 基于改进粒子群算法的天线索网预应力优化[J]. J4, 2013, 47(3): 480-487.