微信服务号

微信订阅号

2025年8月11日 6:18 星期一
首页 > 过刊浏览>2008年第19卷第8期 >1980-1994
PDF HTML阅读 XML下载 导出引用 引用提醒
基于社会网络可视化分析的数据挖掘
作者:
基金项目:

Supported in part by the Key Program of the National Natural Science Foundation of China under Grant Nos.60723003,60505008 (国家自然科学基金);in part by the Natural Science Foundation of Jiangsu Province of China under Grant Nos.BK2007520,BIC2006116 (江苏省自然科学基金);in part by the Australian Research Council (ARC) Centre for Complex Systems under Grant No.CE00348249 (澳大利亚复杂系统研究中心项目)


Networked Data Mining Based on Social Network Visualizations
Author:
  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [21]
    • |
  • 相似文献 [20]
    • |
  • 引证文献
    • | |
  • 文章评论
    摘要:

    把社会等复杂系统看作网络的思想由来已久.利用社会网络分析的方法,能够对各种社会关系进行精确的量化表征和分析,从而揭示其结构,对一系列当代社会的现象进行更加深入而具体的解释.结合社会网络可视化分析和数据挖掘的理论与方法,引入相关的地理信息,对包含1980-2002年间世界范围内1417例恐怖袭击事件的数据库进行教据分析,以这些恐怖袭击事件各要素节点之间关系作为基本分析单位,对恐怖组织之问的活动模式和发展特点等内在规律进行挖掘与解释,得出有意义的结果.提出的方法可以有效地推广应用于蛋白质结构分析、生物基因分析以及各类社会问题的分析过程.

    Abstract:

    Studies in social network theory focus on characterizing complex social relationships by firstly mapping and visualizing them into a graph, and then subsequently identifying the corresponding graph properties. This paper provides an integrated approach, which combines social network analysis and data mining theory with the necessary geographical attributes to analyze 1417 instances of terrorism that occurred world wide during the period 1980-2002. The study reveals interesting patterns on the evolution of these terrorist organizations over two decades. The proposed method can be easily generalized to be applied to other types of large-scale networked datasets, such as micro-array data, and genomic networked data, etc.

    参考文献
    [1]Fayyad U,Piatetsky-Shapiro G,Smyth P.Knowledge discovery and data mining:Towards a unifying framework.In:Fayyad U,Piatetsky-Shapiro G,Smyth P,Uthurusamy R,eds.Advances in Knowledge Discovery and Data Mining.AAI/MIT Press,1996.1-36.
    [2]Staab S.Social networks applied.IEEE Intelligent Systems,1986,124:317-28.
    [3]Kao H,Lin S,Ho J,Chen M.Entropy-Based link analysis for mining Web informative structures.In:Proc.of the 11 th ACM CIKM 2002.New York:ACM Press,2002.574-581.
    [4]Taskem B,Wong M,Abbeel P,Koller D.Label and link prediction in relational data.In:Gottlob G,Walsh T,eds.IJCAI Workshop on Learning Statistical Models from Relational Data.Morgan Kaufmann Publishers,2003.
    [5]Barlow M,Galloway J,Abbass HA.Mining evolution through visualization.In:Beyond Fimess:Visualising Evolution,a Workshop at the 8th Int'l Conf.on the Simulation and Synthesis of Living Systems (ALife 8).MIT Press,2002.103-111.
    [6]Albert R,Barabasi A.Statistical mechanics of complex networks.Reviews of Modem Physics,2002,74:47-97.
    [7]Albert HJR,Barabasi A.Diameter of the world wide web.Nature,1999,401:130-131.
    [8]Wu A,Garland M,Han J.Mining scale-free networks using geodesic clustering.In:Kim W,Kohavi R,Gehrke J,DuMouchel W,eds.Proc.of the 10 th ACM SIGKDD Int'l Conf.on Knowledge Discovery and Data Mining.New York:ACM Press,2004.719-724.
    [9]Software SS.Sampling methods,http://www.statpac.com/surveys/sampling.htm,accessed 2006-08-12.
    [10]Watters J,Biemacki P.Targeted sampling:Options for the study of hidden populations.Social Problems,1989,36(4):416-430.
    [11]Hussain A.Terrorist networks analysis through argument driven hypotheses model.In:ARES 2007:Proc.of the 2nd Int'l Conf.on Availability,Reliability and Security.Washington:IEEE Computer Society Press,2007.480-492.
    [12]Yang CC,Liu N,Sageman M.Analyzing the terrorist social networks with visualization tools.In:Mehrotra S,et al.,eds.Proc.of Intelligence and Security Informatics.Berlin:Springer-Verlag,2006.331-342.
    [13]Lawrence S,Giles CL,Bollacker K.Digital libraries and autonomous citation indexing.IEEE Computer,1999,32(6):67-71.
    [14]Hill S.Social network relational vectors for anonymous identity matching.In:Gottlob G,Waish T,eds.Workshop on Learning Statistical Models from Relational Data,UCAI.Morgan Kaufmann Publishers,2003.
    [15]Popescul A,Ungar L.Statistical relational learning for link prediction.In:Gottlob G,Walsh T,eds.,Workshop on Learning Statistical Models from Relational Data,UCAI.Morgan Kaufmaan Publishers,2003.
    [16]Keim D.Information visualization and visual data mining.IEEE Trans.on Visualization and Computer Graphics,2002,7(1):100-107.
    [17]Honkela T.Self-Organizing maps in natural language proe.[Ph.D.Thesis].Espoo:Helsinki University of Technology,1997.
    [18]Cben J,Sun L,Zaiane OR,Goebel R.Visualizing and discovering web navigational patterns.In:Sihem AY,Luis G,eds.,Proc.of the 7th ACM SIGMOD Int'l Workshop on the Web and Databases (WebDB 2004).New York:ACM Press,2004.13-18.
    [19]Pampalk E,Goebl W,Widmer G.Visualizing changes in the structure of data for exploratory feature selection.In:Getoor L,Senator TE,Domingos P,Faloutsos C,eds.Proc.of the SIGKDD2003.New York:ACM Press,2003.157-166.
    [20]Goldberg H,Wong R.Restructuring transactional data for link analysis in the FinCEN AI system.In:Jensen D,Goldberg H,eds.,1998 Fall Syrup.on Artificial Intelligence and Link Analysis.Menlo Park,California:AAAI Press,1998.38--46.
    [21]Agency CI.The World Factbook.http://www.cia.gov/cia,/publications/factbook,accessed 2006-02-12.
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

杨育彬,李宁,张瑶.基于社会网络可视化分析的数据挖掘.软件学报,2008,19(8):1980-1994

复制
分享
文章指标
  • 点击次数:10871
  • 下载次数: 11266
  • HTML阅读次数: 0
  • 引用次数: 0
历史
  • 收稿日期:2008-04-18
  • 最后修改日期:2008-01-17
文章二维码
友情链接:中国科学院软件研究所中国计算机学会中国科学院国家自然科学基金委员会CCF数字图书馆Int'l J of Softw Info计算机系统应用
您是第20525407位访问者
版权所有：中国科学院软件研究所 京ICP备05046678号-3
地址：北京市海淀区中关村南四街4号,邮政编码：100190
电话：010-62562563 传真：010-62562533 Email：jos@iscas.ac.cn
技术支持：北京勤云科技发展有限公司

京公网安备 11040202500063号