Abstract：Currently, the opportunity of integrating research paradigms of complex networks and data mining has come. On the basis of analyzing and comparing these two paradigms, it is pointed out that data mining research community should pay more attention to discovering universal laws and internal mechanisms of the system. For the research of complex networks, data mining techniques should be introduced to handle big data, and an integrated paradigm for the synergistic collaboration between theoretical model construction and data analytics should be formed. Then, exploratory work of the integration of complex networks and data mining has been discussed, and the possible directions for paradigm integration also have been proposed.
沈斌. 复杂网络与数据挖掘:研究范式的比较和整合[J]. 复杂系统与复杂性科学, 2014, 11(1): 48-52.
SHEN Bin. Comparative Study and Integration of Research Paradigms of Complex Networks and Data Mining[J]. Complex Systems and Complexity Science, 2014, 11(1): 48-52.
[1] 周涛. 网络大数据——复杂网络的新挑战:如何从海量数据获取信息?[J]. 电子科技大学学报, 2013, 42（1):7-8. Zhou Tao. Big data of network:a new challenge for complex network-how to obtain information from massive data[J]. Journal of the University of Electronic Science and Technology of China, 2013, 42（1):7-8. [2] 汪秉宏, 周涛, 周昌松. 人类行为、复杂网络及信息挖掘的统计物理研究[J]. 上海理工大学学报, 2012, 34（2):103-117. Wang Binghong, Zhou Tao, Zhou Changsong. Statistical physics research for human behaviors, complex networks, and information mining[J]. Journal of the University of Shanghai for Science and Technology, 2012, 34（2):103-117. [3] 汪秉宏, 周涛, 刘建国. 推荐系统、信息挖掘及基于互联网的信息物理研究[J]. 复杂系统与复杂性科学, 2010, 7（2/3):46-49. Wang Binghong, Zhou Tao, Liu Jianguo. Recommendation systems, information filtering and internet-based information-physics[J]. Complex Systems and Complexity Science, 2010, 7（2/3):46-49. [4] 李国杰, 程学旗. 大数据研究:未来科技及经济社会发展的重大战略领域——大数据的研究现状与科学思考[J]. 中国科学院院刊, 2012, 27（6):647-657. Li Guojie, Cheng Xueqi. Research status and scientific thinking of big data[J]. Bulletin of Chinese Academy of Sciences, 2012, 27（6):647-657. [5] Han J W, Kamber M. Data Mining:Concepts and Techniques[M]. San Francisco, CA, USA:Morgan Kaufmann Publishers, 2001. [6] Frawley W J, Piatetsky-Shapiro G, Matheus C J. Knowledge discovery in databases:an overview[J]. AI Magazine, 1992, 13（3):57-70. [7] Bengio Y. Learning deep architectures for AI[J]. Foundations and trends in machine learning, 2009, 2（1):1-127. [8] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine:theory and applications[J]. Neurocomputing, 2006, 70（1-3):489-501. [9] Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks[J]. Nature, 1998, 393:440-442. [10] Barabási A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286（5439):509-512. [11] Special Issue on Complex Systems and Networks[J]. Science, 2009, 325（5939):405-432. [12] Barabási A L. The network takeover[J]. Nature Physics, 2012, 8:14-16. [13] 荣智海, 唐明, 汪小帆,等. 复杂网络2012年度盘点[J]. 电子科技大学学报, 2012, 41（6):801-806. Rong Zhihai, Tang Ming, Wang Xiaofan, et al. A survey of the progress of complex networks in 2012[J]. Journal of the University of Electronic Science and Technology of China, 2012, 41（6):801-806. [14] 王文旭. 复杂网络前沿:反问题与控制[R]. 南京:第八届全国复杂网络学术会议, 2012. Wang Wenxu. A frontier of complex network:the inverse problem and the control[R]. Nanjing:The 8th Chinese Conference on Complex Networks （CCCN 2012), 2012. [15] 汪小帆, 李翔, 陈关荣. 网络科学导论[M]. 北京:高等教育出版社, 2012. [16] 方锦清, 李永. 网络科学中统一混合理论模型的若干研究进展[J]. 力学进展, 2008, 38（6):1-16. Fang Jinqing, Li Yong. Advances in unified hybrid theoretical model of network science[J]. Advances in Mechanics, 2008, 38（6):1-16. [17] 方锦清, 毕桥. 统计物理与网络科学面临的若干挑战与思考[J]. 复杂系统与复杂性科学, 2010, 7（4):29-38. Fang Jinqing, Bi Qiao. Several challenge issues for statistical physics and network science[J]. Complex Systems and Complexity Science, 2010, 7（4):29-38. [18] 何大韧, 刘宗华, 汪秉宏. 复杂网络研究的一些统计物理学方法及其背景[J]. 力学进展, 2008, 38（6):1-10. He Daren, Liu Zonghua, Wang Binghong. Some statistical physics methods for investigations of complex networks and their background[J]. Advances in Mechanics, 2008, 38（6):1-10. [19] Costa L da F, Oliveira Jr O N, et al. Analyzing and modeling real-world phenomena with complex networks:a survey of applications[J]. Advances in Physics, 2011, 60（3):329-412. [20] Page L, Brin S, Motwani R, et al. The pagerank citation ranking:bringing order to the web[R]. Technical Report Stanford InfoLab, 1999. [21] Sun Y Z, Han J W. Mining heterogeneous information networks:a structural analysis approach[J]. SIGKDD Explorations, 2012, 14（2):20-28. [22] Sun Y Z, Barber R, Gupta M, et al. Co-author relationship prediction in heterogeneous bibliographic networks[C]//Proc. of the 2011 Int’l Conf. on Advances in Social Networks Analysis and mining. Kaohsiung, Taiwan, China:IEEE Computer Society Washington, DC, 2011:121-128. [23] Kleinberg J. Cascading behavior in networks:algorithmic and economic issues[M]//Noam Nisan, Tim Roughgarden, et al. eds. Algorithmic Game Theory. New York, USA:Cambridge University Press, 2007:613-632. [24] Zhou T, Kuscsik Z, Liu J G, et al. Solving the apprent diversity-accuracy dilemma of recommender systems[J]. Procee-dings of the National Academy of Sciences of the United States of America, 2010, 107（10):4511-4515. [25] Wang W X, Yang R, Lai Y C, et al. Predicting catastrophes in nonlinear dynamical systems by compressive sensing[J]. Physical Review Letters, 2011, 106:154101.
