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  HTML
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.

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.

出版日期: 2013-01-01
:  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/eng/CN/10.3785/j.issn.1008-973X.2013.01.006        http://www.zjujournals.com/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] 赵建军,王毅,杨利斌. 基于时间序列预测的威胁估计方法[J]. J4, 2014, 48(3): 398-403.
[2] 张天煜, 冯华君, 徐之海, 李奇, 陈跃庭. 基于强边缘宽度直方图的图像清晰度指标[J]. J4, 2014, 48(2): 312-320.
[3] 刘中, 陈伟海, 吴星明, 邹宇华, 王建华. 基于双目视觉的显著性区域检测[J]. J4, 2014, 48(2): 354-359.
[4] 崔光茫, 赵巨峰, 冯华君, 徐之海, 李奇, 陈跃庭. 非均匀介质退化图像快速仿真模型的建立[J]. J4, 2014, 48(2): 303-311.
[5] 王相兵,童水光,钟崴,张健. 基于可拓重用的液压挖掘机结构性能方案设计[J]. J4, 2013, 47(11): 1992-2002.
[6] 王进, 陆国栋, 张云龙. 基于数量化一类分析的IGA算法及应用[J]. J4, 2013, 47(10): 1697-1704.
[7] 刘羽, 王国瑾. 以已知曲线为渐进线的可展曲面束的设计[J]. J4, 2013, 47(7): 1246-1252.
[8] 胡根生,鲍文霞,梁栋,张为. 基于SVR和贝叶斯方法的全色与多光谱图像融合[J]. J4, 2013, 47(7): 1258-1266.
[9] 吴金亮, 黄海斌, 刘利刚. 保持纹理细节的无缝图像合成[J]. J4, 2013, 47(6): 951-956.
[10] 陈潇红,王维东. 基于时空联合滤波的高清视频降噪算法[J]. J4, 2013, 47(5): 853-859.
[11] 朱凡,李悦,蒋 凯,叶树明,郑筱祥. 基于偏最小二乘的大鼠初级运动皮层解码[J]. J4, 2013, 47(5): 901-905.
[12] 吴宁, 陈秋晓, 周玲, 万丽. 遥感影像矢量化图形的多层次优化方法[J]. J4, 2013, 47(4): 581-587.
[13] 计瑜,沈继忠,施锦河. 一种基于盲源分离的眼电伪迹自动去除方法[J]. J4, 2013, 47(3): 415-421.
[14] 王翔,丁勇. 基于Gabor滤波器的全参考图像质量评价方法[J]. J4, 2013, 47(3): 422-430.
[15] 童水光, 王相兵, 钟崴, 张健. 基于BP-HGA的起重机刚性支腿动态优化设计[J]. J4, 2013, 47(1): 122-130.