基于Flex的信息可视化框架研究与实现
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摘要
在信息爆炸的时代,数据间的关系和数据的规律常常隐藏于纷繁芜杂的数据海洋中。近年来,与日激增的社会网络数据更是成为现代社会学研究中的研究热点。与此同时,相关科研团队已经开发出功能较为完善的信息可视化分析工具,帮助用户多维度、多侧面的考察分析数据。但是这些工具主要集中在C/S客户端中的应用,对当下流行的Web访问方式支持有限,并且其提供的社会网络分析功能较弱。
本论文针对社会网络数据的可视分析技术展开研究工作,开发基于Adobe Flex技术的信息可视化框架,为融合Web 2.0技术实现信息可视化提供支持。主要工作包括：
1)本文首先对当前的网络可视化技术以及相关可视化工具进行调研,详细探索了布局算法和渲染方式对网络可视化的影响,设计并实现了基于Adobe Flex组件技术的信息可视化框架,对网络数据提供了有效的可视化展示方式,并且通过灵活的用户交互,使得用户可以从不同的可视化形式和维度对网络进行观察分析。
2)为加强针对社会网络数据的分析功能,框架中融入了高效的网络分析算法。主要包括：网络特征统计分析,网络中骨十节点和重要链接发现,网络社区发现与展示,为用户发现网络的结构特性和聚类效应提供有意义的指导。
3)本文将上述的研究应用于国家“十一五”科技支撑计划《科技文献信息服务系统关键技术研究及应用示范》项目中,并已经应用于中国科技分析评价服务平台,验证了本文所研究的信息可视化框架所提供功能的有效性以及对Web访问方式的良好支持。
In the age of information explosion, knowledge and useful laws immerges in the mass of data. In recent years, the increasing network data becoming a hot topic in the modern sociology research. At the same time, scientific research institutions have invented a series of web-based tools to help researchers analyzing data in multi-dimensions and different aspects. However, these tools concentrate more on C/S applications and are not good to Web accessing.
Focused on the mining of social network data, this paper presents a Visual Analysis architecture based on Adobe Flex technology to support the information visualization in Web environment.
This paper is based on the existing network visualization techniques as well as visualization tool for the study, detailed explored the impact of the layout algorithm and rendering method on network visualization, constructed a network visualization model based on Adobe Flex technology, and provided an effective visual display means for network data. The framework not only support a effective multi-dimension dynamic display for existing network, and through a flexible user interaction, allowing users from different attribute dimensions. The framework realized efficient social network analysis algorithms, including statistical analysis of networks, important network nodes and links discovery, community discovery and visualization, thereby achieved more effective analysis of the characters relationship from network data.
Finally, the above research results are applied to China Sci-tech Analysis & Evaluation Service, which is supported by a project called "Sicence and Techonolgy Information Service System key technology research and application demonstration," under national science and technology fund. The results verified the efficiency and effectiveness of the information visualization framework we proposed.
本论文针对社会网络数据的可视分析技术展开研究工作,开发基于Adobe Flex技术的信息可视化框架,为融合Web 2.0技术实现信息可视化提供支持。主要工作包括：
1)本文首先对当前的网络可视化技术以及相关可视化工具进行调研,详细探索了布局算法和渲染方式对网络可视化的影响,设计并实现了基于Adobe Flex组件技术的信息可视化框架,对网络数据提供了有效的可视化展示方式,并且通过灵活的用户交互,使得用户可以从不同的可视化形式和维度对网络进行观察分析。
2)为加强针对社会网络数据的分析功能,框架中融入了高效的网络分析算法。主要包括：网络特征统计分析,网络中骨十节点和重要链接发现,网络社区发现与展示,为用户发现网络的结构特性和聚类效应提供有意义的指导。
3)本文将上述的研究应用于国家“十一五”科技支撑计划《科技文献信息服务系统关键技术研究及应用示范》项目中,并已经应用于中国科技分析评价服务平台,验证了本文所研究的信息可视化框架所提供功能的有效性以及对Web访问方式的良好支持。
In the age of information explosion, knowledge and useful laws immerges in the mass of data. In recent years, the increasing network data becoming a hot topic in the modern sociology research. At the same time, scientific research institutions have invented a series of web-based tools to help researchers analyzing data in multi-dimensions and different aspects. However, these tools concentrate more on C/S applications and are not good to Web accessing.
Focused on the mining of social network data, this paper presents a Visual Analysis architecture based on Adobe Flex technology to support the information visualization in Web environment.
This paper is based on the existing network visualization techniques as well as visualization tool for the study, detailed explored the impact of the layout algorithm and rendering method on network visualization, constructed a network visualization model based on Adobe Flex technology, and provided an effective visual display means for network data. The framework not only support a effective multi-dimension dynamic display for existing network, and through a flexible user interaction, allowing users from different attribute dimensions. The framework realized efficient social network analysis algorithms, including statistical analysis of networks, important network nodes and links discovery, community discovery and visualization, thereby achieved more effective analysis of the characters relationship from network data.
Finally, the above research results are applied to China Sci-tech Analysis & Evaluation Service, which is supported by a project called "Sicence and Techonolgy Information Service System key technology research and application demonstration," under national science and technology fund. The results verified the efficiency and effectiveness of the information visualization framework we proposed.
引文
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[2]G Robertson, S K Card, J D Mackinlay. The Cognitive Co-processor for Interactive User Interfaces [C]. Proceedings of the ACM SIGGRAPH symposium on User interfacs software and technology,1989:10-18.
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[5]毕然,吴斌.融合复杂网络科技文献分析工具综述.数字图书馆论坛,2008
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[11]Anna C. Gilbert,Kirill Levchenko.Compressing Network Graphs. Proceedings of the LinkKDD workshop at the 10th ACM.2004.
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[17]Joshua R. Tyler, Dennis M. Wilkinson, and Bernardo A. Huberman. Email as spectroscopy:Automated discovery of community structure within organiza-tions,2003.
[18]M.E.J.Newman,The structure and function of complex networks. SIAM Review 45,2003,167-256
[19]LC Freeman Visualizing Social Networks Journal of social structure,2000
[20]Bender-deMoll, S., and McFarland, D. The art and science of dynamic network visualization. Journal of Social Structure,7(2),2006.
[21]Richard A. Becker, Stephen G. Eick, Eileen O. Miller, Allan R. Wilks, Dynamic graphics for network visualization, Proceedings of the 1st conference on Visualization'90, October 23-26,1990
[22]James Moody Dynamic Network Visualization AJS Volume 110 Number 4,2005,1206-41,
[23]Weiwei Cui; Hong Zhou; Huamin Qu Geometry-Based Edge Clustering for Graph Visualization IEEE transactions on visualization and computer graphics Vol.14(No.6),2008
[24]Michael Kaufmann,Dorothea Wagner Graph drawing:14th international symposium, GD 2006, Karlsruhe, Germany 2006
[25]Ka-Ping Yee Fisher, D. Dhamija, R. Animated exploration of dynamic graphs with radial layout Information Visualization,. INFOVIS 2001. IEEE Symposium on 200143-50,2001
[26]Eades P. A heuristic for graph drawing. Congressus Nutnerantiunt 42, 1984,149～160,
[27]Kamada T, Kawai S. An Algorit hm for Drawing General Undirected Graphs. Information Processing Let ters,31,1989,7～15
[28]Fruchterman T MJ, Reingold E M. Graph Drawing by Force2Directed Placement. Sof tware Practice and Experience,,21(11),1991,1129～1164
[29]MacEachren, A.M.. How Maps Work. The Guilford Press, New York, USA. 1995
[30]Muller, W. Schumann, H.2003 Visualization methods for time-dependent data-an overview Simulation Conference,2003.737-745
[31]Gerald M. Karam Visualization using timelines nternational Symposium on Software Testing and Analysis:Proceedings of the 1994 ACM SIGSOFT international symposium on Software testing and analysis; 1994,125-137
[32]Hopcroft J, KhanO, Kulis B,.Tracking evolving communities inl arge linked networks.PNAS,,101(1-1),20045249-5253
[33]Ntoulas A,Cho J, O lston C. Whats new on the Web The evolution of Web from a Search Engine perspective.2004 Proceedings of the Thirteenth International Conference on World Wide Web,2004,1-12
[34]http://arnetminer.org/AcademicStatistics
[35]http://libra.msra.cn/
[36]Object-Level Ranking:Bringing Order to Web Objects Zaiqing Nie, Yuanzhi Zhang, Ji-Rong Wen, and Wei-Ying Ma In the Proceedings of the 14th international World Wide Web conference (WWW 2005), May 10-14,2005, in Chiba, Japan.
[37]Zheng, J. and Nie, Z.2009. Architecture and Implementation of Object-Level Vertical Search. In Proceedings of the 2009 international Conference on New Trends in information and Service Science (June 30-July 02,2009). NISS. IEEE Computer Society, Washington, DC,264-268.
[38]http://dblpvis.uni-trier.de/
[39]www.cdblp.cn
[40]http://www.gopubmed.org/web/gopubmed/
[41]Ignacio Alvarez Hamelina, Luca Dall'Asta,Alain Barrat. k-core decomposition:a tool for the visualization of large scale networks. Advances in Neural Information Processing Systems 18, Canada (2006) 41
[42]Davidson, Ron, Harel, David.Drawing graphs nicely using simulated annealing. ACM Transactions on Graphics. Vol.15, no.4, pp.1996,301-331.
[43]Carlis, J.V., and J.A. Konstan.. Interactive visualiza-tion of serial periodic data. Proc.11th annual ACM symposium on User interface software and technology. San Francisco, California, USA.1998,29-38.
[2]G Robertson, S K Card, J D Mackinlay. The Cognitive Co-processor for Interactive User Interfaces [C]. Proceedings of the ACM SIGGRAPH symposium on User interfacs software and technology,1989:10-18.
[3]B. Shneiderman. The eye have it:A task by data type taxon omy for information visualizations IEEE Symposium on Visual Languages.1996
[4]王柏,吴巍,徐超群,吴斌.复杂网络可视化研究综述.计算机科学,2007
[5]毕然,吴斌.融合复杂网络科技文献分析工具综述.数字图书馆论坛,2008
[6]Prefuse:http://prefuse.org/
[7]jung:http://jung.sourceforge.net/
[8]Q Ye, B Wu, B Wang. JSNVA:A Java Straight-Line Drawing Framework for Network Visual Analysis. Advanced Data Mining and Applications,4th International Conference, ADMA 2008,
[9]Battista GD, Eades P, Tamassia R. Algorithms for Drawing Graphs:an Annotated Bibliography. Computational Geometry:Theory and Applications archive. Volume 4, Issue 5.1994
[10]Guy Melanc Eon, and M. Scott Marshall.Graph Visualization and Navigation in Information Visualization:a Survey. IEEE Transactions on 24 IEEE TRANSACTIONS ON VISUALIZATION.2000
[11]Anna C. Gilbert,Kirill Levchenko.Compressing Network Graphs. Proceedings of the LinkKDD workshop at the 10th ACM.2004.
[12]Tomas Feder Harry Road. San Jose.Clique partitions, graph compression and speeding Journal of Computer and System Sciences archive. Volume 51, Issue 2 October 1995
[13]The Network Workbench (NWB).http://nwb.slis.indiana.edu
[14]SoNIA. http://www.stanford.edu/group/sonia/
[15]Pajek. http://vlado.fmf.uni-lj.si/pub/networks/pajek/
[16]Moreno,J.L.Who Shall Survive:Foundations of Sociometry, Group Psychotherapy, and Sociodrama. Beacon House.1953.
[17]Joshua R. Tyler, Dennis M. Wilkinson, and Bernardo A. Huberman. Email as spectroscopy:Automated discovery of community structure within organiza-tions,2003.
[18]M.E.J.Newman,The structure and function of complex networks. SIAM Review 45,2003,167-256
[19]LC Freeman Visualizing Social Networks Journal of social structure,2000
[20]Bender-deMoll, S., and McFarland, D. The art and science of dynamic network visualization. Journal of Social Structure,7(2),2006.
[21]Richard A. Becker, Stephen G. Eick, Eileen O. Miller, Allan R. Wilks, Dynamic graphics for network visualization, Proceedings of the 1st conference on Visualization'90, October 23-26,1990
[22]James Moody Dynamic Network Visualization AJS Volume 110 Number 4,2005,1206-41,
[23]Weiwei Cui; Hong Zhou; Huamin Qu Geometry-Based Edge Clustering for Graph Visualization IEEE transactions on visualization and computer graphics Vol.14(No.6),2008
[24]Michael Kaufmann,Dorothea Wagner Graph drawing:14th international symposium, GD 2006, Karlsruhe, Germany 2006
[25]Ka-Ping Yee Fisher, D. Dhamija, R. Animated exploration of dynamic graphs with radial layout Information Visualization,. INFOVIS 2001. IEEE Symposium on 200143-50,2001
[26]Eades P. A heuristic for graph drawing. Congressus Nutnerantiunt 42, 1984,149～160,
[27]Kamada T, Kawai S. An Algorit hm for Drawing General Undirected Graphs. Information Processing Let ters,31,1989,7～15
[28]Fruchterman T MJ, Reingold E M. Graph Drawing by Force2Directed Placement. Sof tware Practice and Experience,,21(11),1991,1129～1164
[29]MacEachren, A.M.. How Maps Work. The Guilford Press, New York, USA. 1995
[30]Muller, W. Schumann, H.2003 Visualization methods for time-dependent data-an overview Simulation Conference,2003.737-745
[31]Gerald M. Karam Visualization using timelines nternational Symposium on Software Testing and Analysis:Proceedings of the 1994 ACM SIGSOFT international symposium on Software testing and analysis; 1994,125-137
[32]Hopcroft J, KhanO, Kulis B,.Tracking evolving communities inl arge linked networks.PNAS,,101(1-1),20045249-5253
[33]Ntoulas A,Cho J, O lston C. Whats new on the Web The evolution of Web from a Search Engine perspective.2004 Proceedings of the Thirteenth International Conference on World Wide Web,2004,1-12
[34]http://arnetminer.org/AcademicStatistics
[35]http://libra.msra.cn/
[36]Object-Level Ranking:Bringing Order to Web Objects Zaiqing Nie, Yuanzhi Zhang, Ji-Rong Wen, and Wei-Ying Ma In the Proceedings of the 14th international World Wide Web conference (WWW 2005), May 10-14,2005, in Chiba, Japan.
[37]Zheng, J. and Nie, Z.2009. Architecture and Implementation of Object-Level Vertical Search. In Proceedings of the 2009 international Conference on New Trends in information and Service Science (June 30-July 02,2009). NISS. IEEE Computer Society, Washington, DC,264-268.
[38]http://dblpvis.uni-trier.de/
[39]www.cdblp.cn
[40]http://www.gopubmed.org/web/gopubmed/
[41]Ignacio Alvarez Hamelina, Luca Dall'Asta,Alain Barrat. k-core decomposition:a tool for the visualization of large scale networks. Advances in Neural Information Processing Systems 18, Canada (2006) 41
[42]Davidson, Ron, Harel, David.Drawing graphs nicely using simulated annealing. ACM Transactions on Graphics. Vol.15, no.4, pp.1996,301-331.
[43]Carlis, J.V., and J.A. Konstan.. Interactive visualiza-tion of serial periodic data. Proc.11th annual ACM symposium on User interface software and technology. San Francisco, California, USA.1998,29-38.