基于用户特征的社交网络数据挖掘研究
|
摘要
数据是互联网中最宝贵的资源之一,海量数据中蕴含着巨大的潜在价值,深入挖掘这些数据对于互联网电子商务、企业决策与推广、信息传播与预测均具有重要的意义。随着Web2.0网络应用与移动终端设备的发展,社交网络的普及率与使用率日益提高。相比传统网络应用形式,社交网络具有用户主体性强、网络特征多样、数据内容丰富、群体交互密切、信息传播迅速等特点。传统的研究方法与模型难以准确地描述社交网络中用户的行为特征,因而难以实现符合社交网络特性的数据挖掘与分析。鉴于此,论文结合交叉学科的研究方法,针对现有算法与模型运用于社交网络时存在的效果与性能问题,分别从互联网数据采集与处理、社交网络数据实证分析、用户影响力与行为分析、用户个性化推荐算法以及基于机器学习的信息预测算法等角度,对社交网络中的数据挖掘方法进行了研究。
论文的研究工作得到了国家自然科学基金项目(No.61172072、61271308)、北京市自然科学基金项目(No.4112045)和高等学校博士学科点专项科研基金(No.20100009110002)的支持,论文的主要研究内容如下：
1.研究了互联网数据采集与预处理技术。针对数据挖掘相关研究对于数据样本精度与模型处理性能的具体要求,提出了一套数据抓取与处理的完整方案。首先,优化了基于Nutch的分布式网络爬虫系统,实现了爬虫系统的并行化同步运行方式,提升了爬虫处理性能。之后,重点研究了网页信息解析算法,提出了基于规则与基于wrapper的网页解析模型。基于规则的网页解析模型逻辑简单且普适性强,适用于互联网海量网页的处理工程；基于wrapper的网页解析模型具有较高的信息抽取精度,且能够实现来自相同网站信息的结构化处理。最后,研究了网页快速消重算法与自动摘要算法,以到达降低样本特征的数量与维度,提高数据质量的目的。
2.实证分析了微博社交网络特征与用户特征。对新浪微博在线数据进行了多维度分析,包括用户特征、微博特征、时间与演化特征等,探讨了作用于用户影响力与微博传播关系的主要因素。在上述分析的基础上,提出了一套微博社交网络用户权重计算模型。该模型由用户活跃度特征与基于HITS算法的用户影响力特征加权实现,并在数据分析的基础上改进了HITS算法的实现方式,降低了传统HITS模型用于迭代的运算时间。社交网络中更强调人与人的交互关系,本文用户权威性分析,为进一步研究社交网络中的信息推荐与传播机制提供了理论基础。
3.研究了社交网络中的用户个性化推荐算法。针对传统推荐算法不足以描述社交网络中的用户偏好性问题,提出了基于统计特征的微博推荐算法。该算法由用户微博内容偏好性、微博作者影响力水平与用户交互关系三大特征加权构成,算法逻辑简单,计算性能较高,适用于在线微博平台的应用级研究。为进一步提高模型的推荐精度,论文借助基于二元网络的NBI推荐模型,对NBI模型初始矩阵与计算中连接权重进行了优化,并将具有社交网络特色的用户特征对于微博的偏好性影响加入到模型中,实现了微博的个性化推荐。试验结果表明,该算法相比NBI模型或单一偏好特征推荐模型,具有更好的个性化推荐效果。
4.提出了基于机器学习的信息预测方法。结合微博社交网络的真实数据,分析了影响用户连接关系以及微博传播的主要特征因素,建立了基于SVM的用户连接关系预测模型与基于逻辑回归的用户微博转发模型。为提高算法的预测性能与big-data模式下模型的实现方式,初步探讨了相关机器学习模型的并行化参数训练方法,提出了SVM模型的松弛变量权重优化算法,提升了模型的预测精度。最后,以用户微博转发模型计算结果作为个体决策先验概率,利用蒙特卡罗仿真方法模拟了微博在社交网络中的传播过程。该方法通过微观个体决策模型,结合全局仿真,不但能够预测信息的宏观传播趋势,还可以发现传播路径中可能存在的关键用户节点,为信息的传播预测研究提供了参考和借鉴。
As one of the most valuable resources of the internet, data usually contain important information and can be utilized for many ways. Thereby data mining is significant for e-commerce, enterprise strategy and promotion as well as information diffusion and prediction. With the development of Web2.0techniques and mobile terminals, social network services are increasing their popularities and utilizations in people's daily life. Compared with the traditional networks, it emphasizes more on user's proactive role, diversity of network features, huge information, user interaction&reciprocity, and fast message propagation in social networks. Traditional approaches and models are inadequate to describe user behavior features. Therefore current methods are insufficient for data analysis and mining in SNS networks. In view of this, this paper utilizes interdisciplinary methods and theories to study on the researches of data mining in social networks, including data retrieval and preprocessing, network analysis, user influence and behavior, personalized recommendation, and machine learning based prediction methods, in order to enhance efficiency and effectiveness of the existing algorithms and models.
The work of the dissertation is supported by the National Natural Science Foundation of China (No.61172072,61271308), Beijing Natural Science Foundation (No.4112045), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20100009110002). Main contributions of the dissertation are as follows:
1. We research on information retrieval and data preprocessing techniques. Due to the idiographic requirements on data accuracy and computational performance of the models, we propose an integrated framework of data retrieval. Firstly, we optimize the distributed web crawler based on Nutch system by a synchronous operational architecture. This improvement can enhance the efficiency of the web crawler. Then we study on information extraction methods and propose two webpage extraction models based on rules and wrappers. Rules based extraction model is pervasive and has a simple computational complexity which is applicable for mass information process in the internet. Wrappers based extraction model can implement a highly accurate data retrieval and realize a structurized information extraction within the same domain. Besides, we research on rapid webpage de-duplication algorithms and automatic summarization algorithms for the purposes of reducing the data magnitude&dimensionality and enhancing the quality of the information.
2. We empirically analyze the network features of SINA Microblog social network including user characters, tweet features, network evolvement, etc and discuss the dominating factors which act on user influence and tweet oriented information dissemination. Motivated by statistical results and conclusions above, we establish a user weight model for Microblog networks. This model is composed by user active degree and HITS based user influence factor. We improve the HITS algorithm by eliminating the iteration in node authority calculation process. The interaction among users is one of the most important identities in social networks. Therefore the analysis of user authority can take an active role in the researches of information recommendation and diffusion.
3. We study on personalized recommendation algorithms in social networks. For the problem that the existing recommendation models can hardly describe user preferences in social networks, we introduce a tweet recommendation algorithm based on statistical features for Microblog networks. This algorithm combines content similarity, author influence and user reciprocity. It has a low computational complexity and is adaptive for the applications of real online Microblog systems. In order to enhance the recommendation accuracy, we employ a bipartite network based model by which is named NBI into our tweet oriented recommendation research. We improve the traditional NBI model by the original matrix and the link weights in the resource allocation processes. We combine the improved NBI model with user features for the final model which can eventually address the distinct characteristics for social network recommendations. The experimental results illustrate that the effectiveness of our proposed model is better than either the traditional NBI model or singe preference based recommendation model.
4. We propose the information prediction approaches based on machine learning algorithms. According to the empirical analysis of SINA Microblog network, we confirm and quantitate the exact features for the eigenvector of the data samples. Consequentially we establish the user link prediction model based on logistic regression and the user re-tweet model based on SVM. In order to improve the classification accuracy and enable a robust implementation for big data, we preliminarily explore the parallel computing patterns for the relative machine learning models in the parameter training process, and optimize the coefficient weight of the slack variable in SVM model. Eventually, we employ the computational result of the user re-tweet model as the prior probability for an arbitrary node, and then utilize Monte Carlo method to simulate the tweets propagation process in SINA Microblog. This method is based on the microcosmic user model, and integrates with the macroscopic simulation of the information dissemination. Therefore, it can not only predict the general trends of a web topic, but also can discover the key users in the information diffusion trace. These approaches could provide positive ideas for the researches of information dissemination and prediction.
论文的研究工作得到了国家自然科学基金项目(No.61172072、61271308)、北京市自然科学基金项目(No.4112045)和高等学校博士学科点专项科研基金(No.20100009110002)的支持,论文的主要研究内容如下：
1.研究了互联网数据采集与预处理技术。针对数据挖掘相关研究对于数据样本精度与模型处理性能的具体要求,提出了一套数据抓取与处理的完整方案。首先,优化了基于Nutch的分布式网络爬虫系统,实现了爬虫系统的并行化同步运行方式,提升了爬虫处理性能。之后,重点研究了网页信息解析算法,提出了基于规则与基于wrapper的网页解析模型。基于规则的网页解析模型逻辑简单且普适性强,适用于互联网海量网页的处理工程；基于wrapper的网页解析模型具有较高的信息抽取精度,且能够实现来自相同网站信息的结构化处理。最后,研究了网页快速消重算法与自动摘要算法,以到达降低样本特征的数量与维度,提高数据质量的目的。
2.实证分析了微博社交网络特征与用户特征。对新浪微博在线数据进行了多维度分析,包括用户特征、微博特征、时间与演化特征等,探讨了作用于用户影响力与微博传播关系的主要因素。在上述分析的基础上,提出了一套微博社交网络用户权重计算模型。该模型由用户活跃度特征与基于HITS算法的用户影响力特征加权实现,并在数据分析的基础上改进了HITS算法的实现方式,降低了传统HITS模型用于迭代的运算时间。社交网络中更强调人与人的交互关系,本文用户权威性分析,为进一步研究社交网络中的信息推荐与传播机制提供了理论基础。
3.研究了社交网络中的用户个性化推荐算法。针对传统推荐算法不足以描述社交网络中的用户偏好性问题,提出了基于统计特征的微博推荐算法。该算法由用户微博内容偏好性、微博作者影响力水平与用户交互关系三大特征加权构成,算法逻辑简单,计算性能较高,适用于在线微博平台的应用级研究。为进一步提高模型的推荐精度,论文借助基于二元网络的NBI推荐模型,对NBI模型初始矩阵与计算中连接权重进行了优化,并将具有社交网络特色的用户特征对于微博的偏好性影响加入到模型中,实现了微博的个性化推荐。试验结果表明,该算法相比NBI模型或单一偏好特征推荐模型,具有更好的个性化推荐效果。
4.提出了基于机器学习的信息预测方法。结合微博社交网络的真实数据,分析了影响用户连接关系以及微博传播的主要特征因素,建立了基于SVM的用户连接关系预测模型与基于逻辑回归的用户微博转发模型。为提高算法的预测性能与big-data模式下模型的实现方式,初步探讨了相关机器学习模型的并行化参数训练方法,提出了SVM模型的松弛变量权重优化算法,提升了模型的预测精度。最后,以用户微博转发模型计算结果作为个体决策先验概率,利用蒙特卡罗仿真方法模拟了微博在社交网络中的传播过程。该方法通过微观个体决策模型,结合全局仿真,不但能够预测信息的宏观传播趋势,还可以发现传播路径中可能存在的关键用户节点,为信息的传播预测研究提供了参考和借鉴。
As one of the most valuable resources of the internet, data usually contain important information and can be utilized for many ways. Thereby data mining is significant for e-commerce, enterprise strategy and promotion as well as information diffusion and prediction. With the development of Web2.0techniques and mobile terminals, social network services are increasing their popularities and utilizations in people's daily life. Compared with the traditional networks, it emphasizes more on user's proactive role, diversity of network features, huge information, user interaction&reciprocity, and fast message propagation in social networks. Traditional approaches and models are inadequate to describe user behavior features. Therefore current methods are insufficient for data analysis and mining in SNS networks. In view of this, this paper utilizes interdisciplinary methods and theories to study on the researches of data mining in social networks, including data retrieval and preprocessing, network analysis, user influence and behavior, personalized recommendation, and machine learning based prediction methods, in order to enhance efficiency and effectiveness of the existing algorithms and models.
The work of the dissertation is supported by the National Natural Science Foundation of China (No.61172072,61271308), Beijing Natural Science Foundation (No.4112045), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20100009110002). Main contributions of the dissertation are as follows:
1. We research on information retrieval and data preprocessing techniques. Due to the idiographic requirements on data accuracy and computational performance of the models, we propose an integrated framework of data retrieval. Firstly, we optimize the distributed web crawler based on Nutch system by a synchronous operational architecture. This improvement can enhance the efficiency of the web crawler. Then we study on information extraction methods and propose two webpage extraction models based on rules and wrappers. Rules based extraction model is pervasive and has a simple computational complexity which is applicable for mass information process in the internet. Wrappers based extraction model can implement a highly accurate data retrieval and realize a structurized information extraction within the same domain. Besides, we research on rapid webpage de-duplication algorithms and automatic summarization algorithms for the purposes of reducing the data magnitude&dimensionality and enhancing the quality of the information.
2. We empirically analyze the network features of SINA Microblog social network including user characters, tweet features, network evolvement, etc and discuss the dominating factors which act on user influence and tweet oriented information dissemination. Motivated by statistical results and conclusions above, we establish a user weight model for Microblog networks. This model is composed by user active degree and HITS based user influence factor. We improve the HITS algorithm by eliminating the iteration in node authority calculation process. The interaction among users is one of the most important identities in social networks. Therefore the analysis of user authority can take an active role in the researches of information recommendation and diffusion.
3. We study on personalized recommendation algorithms in social networks. For the problem that the existing recommendation models can hardly describe user preferences in social networks, we introduce a tweet recommendation algorithm based on statistical features for Microblog networks. This algorithm combines content similarity, author influence and user reciprocity. It has a low computational complexity and is adaptive for the applications of real online Microblog systems. In order to enhance the recommendation accuracy, we employ a bipartite network based model by which is named NBI into our tweet oriented recommendation research. We improve the traditional NBI model by the original matrix and the link weights in the resource allocation processes. We combine the improved NBI model with user features for the final model which can eventually address the distinct characteristics for social network recommendations. The experimental results illustrate that the effectiveness of our proposed model is better than either the traditional NBI model or singe preference based recommendation model.
4. We propose the information prediction approaches based on machine learning algorithms. According to the empirical analysis of SINA Microblog network, we confirm and quantitate the exact features for the eigenvector of the data samples. Consequentially we establish the user link prediction model based on logistic regression and the user re-tweet model based on SVM. In order to improve the classification accuracy and enable a robust implementation for big data, we preliminarily explore the parallel computing patterns for the relative machine learning models in the parameter training process, and optimize the coefficient weight of the slack variable in SVM model. Eventually, we employ the computational result of the user re-tweet model as the prior probability for an arbitrary node, and then utilize Monte Carlo method to simulate the tweets propagation process in SINA Microblog. This method is based on the microcosmic user model, and integrates with the macroscopic simulation of the information dissemination. Therefore, it can not only predict the general trends of a web topic, but also can discover the key users in the information diffusion trace. These approaches could provide positive ideas for the researches of information dissemination and prediction.
引文
[1]中国互联网络信息中心(CNNIC).第31次中国互联网络发展状况统计报告[R].2013.http://www.cnnic.cn/hlwfzyj/hlwxzbg/hlwtjbg/201301/P020130122600399530412.pdf。
[2]N. Pieter, H. Michiel. Mining Twitter in the Cloud:A Case Study [C]. CLOUD 2010, Miami, FL, United states, IEEE Computer Society,2010:107-114.
[3]R. Abraham, T. Martinez. Twitter:Network Properties Analysis [C]. CONIELECOMP 2010, Cholula Puebla, Mexico, IEEE Computer Society,2010:180-184.
[4]B. Kozma, A. Barrat. Consensus formation on adaptive networks [J]. Physical Review E,2008, 77,016102.
[5]汪小帆,李翔,陈关荣.复杂网络理论及其应用[M].北京：清华大学出版社,2006.
[6]R. Albert, A. L. Barabasi. Statistical Mechanics of Complex Networks [J]. Review of Modern Physics,2002,74,47-97.
[7]A. Barrat, M. Barthelemy, A. Vespignani. Modeling the evolution of weighted networks [J]. Physical Review E,2004,70,066149.
[8]R. Pastor-Satorras, A. Vazquez, A. Vespignani. Dynamical and Correlation Properties of the Internet [J]. Physical Review Letters,2001,87,258701.
[9]M. E. Newman. Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality [J]. Physical Review E,2001,64,016132.
[10]M.E. Newman. The structure of scientific collaboration networks [J]. Proceedings of the National Academy of Sciences of the United States of America,2001,98,404.
[11]H. Dietz, M. Rief. Elastic Bond Network Model for Protein Unfolding Mechanics [J]. Physical Review Letters,2008,100,098101.
[12]P. Erd6s, A. R6nyi. On the evolution of random graphs [J]. Publ. Math. Inst. Hung. Acad. Sci., 1960,5,17-60.
[13]D. J. Watts, S. H. Strogatz. Collective dynamics of 'small-world' networks [J]. Nature,1998,393, 440-442.
[14]M. E. Newman, D. J. Watts. Renormalization group analysis of the small-world network model [J]. Physics Letters A,1999,263,341-346.
[15]潘灶烽,汪小帆.一种可大范围调节聚类系数的加权无标度网络模型[J].物理学报,2006,55(8),4058-4064.
[16]A. L. Barabasi, R. Albert. Emergence of scaling in random networks [J]. Science,1999,286 (5439),509-512.
[17]李季,肖井华,蒋品群.节点数加速增长的复杂网络生长模型[J].物理学报,2006,55(8)：4051-4057.
[18]J. Jost, M. P. Joy. Evolving networks with distance preferences [J]. Physical Review E,2002,66, 036126.
[19]K. Klemm, V. E. Eguiluz. Highly clustered scale-free networks [J]. Physical Review E,2002,65, 036123.
[20]C. Tang, Y. Tang. Evolving Networks with Nonlinear Assignment of Weight [J]. Chinese Physics Letters,2006,23(1),259-262.
[21]X. P. Xu, F. Liu, W. Li. Growing Small-World Networks Based on a Modified BA Model [J]. Chinese Physics Letters,2006,23(3),750-753.
[22]S. Brin, L. Page. The anatomy of a large-scale hypertextual web search engine [J]. Computer Networks and ISDN Systems,1998,30,107-117.
[23]J. Kleinberg. Authoritative sources in a hyperlinked environment [J]. Journal of the ACM,1999, 46(5),604-632.
[24]J. L. Zhang, Y. J. Ren. Webs ranking model based on pagerank algorithm [C]. Information Science and Engineering 2010,4811-4814.
[25]S. Cheng, Y. T. Pan. PageRank, HITS and Impact Factor for Journal Ranking [C]. Computer Science and Information Engineering,2009 WRI World Congress on.2009,6,285-290.
[26]Y. Tong. Apply HITS Algorithm to Measuring Class's Authoritative Complexity [C]. WiCom 2008,1-4.
[27]A. Leavitt, E. Burchard, D. Fisher, S. Gilbert. The Influential:New Approaches for Analyzing Influence on Twitter [C]. Web Ecology Project 2009,4(2),1-18.
[28]A. D. Sarma. Ranking Mechanisms in Twitter-like Forums [C]. ACM, Web search and data mining,2010,21-30.
[29]M. Cha. Measuring User Influence in Twitter:The Million Follower Fallacy [C]. Proceedings of international AAAI Conference on Weblogs and Social,2010,10-17.
[30]R. Wang, Y. S. Jin. An Empirical Study on the Relationship between the Followers' Number and Influence of Microblogging [C]. ICEE,2010,509,2014-2017.
[31]R. Nagmoti. Ranking Approaches for Microblog Search [C]. WI-IAT,2010,170,153-157.
[32]J. S. Weng, E. P. Lim, J. Jiang, Q. He, TwitterRank:Finding Topic-Sensitive Influential Twitterers [C]. ACM,Web search and data mining,2010,261-270.
[33]Y. C. Zhang, Y. Liu, H Cheng, F Xiong, C. L. Zhang. A Method of Measuring User Influence in MicroBlog [J]. Journal of Convergence Information Technology,2011,6(10),243-250.
[34]A. Pal. Identifying Topical Authorities in Microblogs. ACM,Web search and data mining,2011, 45-54.
[35]R. X. Han. The Influence of Microblogging on Personal Public Participation [C]. Web Society (SWS),2010 IEEE 2nd Symposium, IEEE,2010,615-618.
[36]S. L. Kang, C. Zhang. Complexity Research of Massively Microblogging Based on Human Behaviors [C]. DBTA,2010,1-4.
[37]R. T. Glinton, P. Scerri, K. Sycara. Towards the understanding of information dynamics in large scale networked systems [C].12th International Conference on Information Fusion, FUSION, 2009,794-801.
[38]C. Canali, M. Colajanni, R. Lancellotti. Characteristics and evolution of content popularity and user relations in social networks [C]. Computers and Communications (ISCC),2010 IEEE Symposium,2010,750-756.
[39]F. Benevenuto, T. Rodrigues, M. Cha, V. Almeida. Characterizing user behavior in online social networks [C]. ACM SIGCOMM conference on Internet measurement conference,2009,49-62.
[40]A. Grabowski. Interpersonal interactions and human dynamics in a large social network [J]. Physica A:Statistical Mechanics and its Applications,2007,385(1),363-369.
[41]K. Naruse, M. Kubo. Lognormal distribution of BBS articles and its social and generative mechanism [C]. Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence,2006,103-112.
[42]A. Kaltenbrunner, V. Gomez, V. L6pez. Description and prediction of slashdot activity [C]. Proceedings of the 2007 Latin American Web Conference,2007,57-66.
[43]F. Ding, Y. Liu, B. Shen, H. Cheng. Modeling the reading and replying activities in a BBS social network [J]. Journal of Electronic Science Technology,2010,8(4),300-306.
[44]M. Jamali, H. Sayyadi, B. B. Hariri. A Method for Focused Crawling Using Combination of Link Structure and Content Similarity [C]. Web Intelligence,2006,753-756.
[45]J. Cho, Crawling the web:Discovery and Maintenance of Large Scaled Web Data [D]. Computer Science,2001.
[46]J. J. Tsay, C. Y. Shih, B. L. Wu, Auto crawler:An Integrated System for Automatic Topical Crawler [J]. Computer and Information Science,2005,462-467.
[47]张彦超,刘云.基于自动生成模板的Web信息抽取技术研究[J].北京交通大学学报,2009,33(5),40-45.
[48]欧健文,董守斌.模板化网页主题信息的提取方法[J],清华大学学报,2005,45(09),1743-1747.
[49]杨少华,林海略,韩燕波.针对模板生成网页的一种数据自动抽取方法[J].软件学报,2008,19(2),209-223.
[50]周明建,高济,李飞.基于本体论的Web信息抽取[J].计算机辅助设计与图形学学报,2004,16(4),535-541.
[51]F. Ashraf F, T. yzyer, R. Alhajj. Employing Clustering Techniques for Automatic Information Extraction From HTML Documents [J], Systems, Man, and Cybernetics, Part C:Applications and Reviews, IEEE Transactions,2008,38(5),660-673.
[52]张志刚,陈静,李晓明.一种html网页净化方法[J].情报学报,2004,23(4),387-393.
[53]J. Y. Wang, F. H. Lochovsky. Data-rich Section Extraction from HTML pages [C]. Proc 3rd Int Conf on Web Info SystEng(WISE.02), IEEE Computer Society Press,2002,313-322.
[54]李效东,顾毓清.基于DOM的Web信息提取[J].计算机学报,2002,25(5),526-533.
[55]李泽峰,王煜.基于RBF神经网络和关联规则的Web文本分类规则获取方法[J].图书情报工作,2006,50(10),90-92.
[56]M.X. Zhu, Z. Cai, Q. S. Cai. Automatic keywords extraction of Chinese document using small worldstructure [C]. Natural Language Processing and Knowledge Engineering,2003,438-443.
[57]罗可,林睦纲,郗东妹.数据挖掘中分类算法综述[J].计算机工程,2005,31(1),3-5.
[58]林鸿飞.基于示例的文本标题分类机制[J].计算机研究与发展,2001,38(9),1132-1136.
[59]张蓉.数据聚类技术的研究[J].计算机工程与应用.2002,16,145-147.
[60]吴艳文,胡学钢,王东波.基于特征加权的k-modes聚类应用[J].中国科技信息,2007,16,145.
[61]I. S. Dhillon, D. S. Modha. Concept Decompositions for Large Sparse Text Data Using Clustering [J]. Machine Learning,2011,42(1-2),143-175.
[62]Z. X. Huang. Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values [J]. Data Mining and Knowledge Discovery,1998,2(3),283-304.
[63]刘挺,王开铸.自动文摘的四种主要方法[J],情报学报,1999,18(1),10-19.
[64]Y. H. Zhang, J. Y. Guo, H. M. Gong, Z. S. Xue, Y. M. Zhang. Single-Document Automatic Abstracting System Based on Topic Partition [C]. Intelligent Information Technology Application Workshops,2008,280-283.
[65]L. Zhou, E. Hovy. On the Summarization of Dynamically Introduced Information:Online Discussions and Blogs [C]. Proceedings of AAA1-2006 Spring Symposium on Computational Approaches to Analyzing Weblogs,2006,237-242.
[66]H. Jiang, C. J. Peng. Automatic Text Summarization Based on Thematic Word Weight and Sentence Features [J]. Journal of South China University of Technology,2010,7,012.
[67]L. Lu, M. Medo, C. H. Yeung. Recommender systems [J]. Physics Reports,2012,519,1-49.
[68]Y. Hu, Y. Koren, C. Volinsky. Collaborative filtering for implicit feedback datasets:Data Mining [C]. ICDM '08, Eighth IEEE International Conference,2008,263-272.
[69]B. Sarwar, G. Karypis, J. Konstan. Application of dimensionality reduction in recommender system-a case study [M].DTIC Document,2000.
[70]B. Sarwar, G. Karypis, J. Konstan. Item-based collaborative filtering recommendation algorithms [C]. Proceedings of the 10th international conference on World Wide Web, ACM, 2001,285-295.
[71]D. Goldberg, D. Nichols, B. M. Oki. Using collaborative filtering to weave an information tapestry [J]. Communications of the ACM,1992,3 5(12),61-70.
[72]P. Resnick, N. Iacovou, M. Suchak. GroupLens:an open architecture for collaborative filtering of netnews [C]. ACM conference on Computer supported cooperative work,1994,175-186.
[73]H. C. Chen, A. Chen. A music recommendation system based on music data grouping and user interests [C]. Conference on Information and Knowledge Management:Proceedings of the tenth international conference on Information and knowledge management,2001,5(10),231-238.
[74]H. Thomas. Latent semantic models for collaborative filtering [J]. ACM Transactions on Information Systems (TOIS),2004,22(1),89-115.
[75]M. Balabanovic, Y. Shoham. Fab:Content-Based Collaborative Recommendation [J]. Communication of the ACM,1997,40(3),66-72..
[76]F. Abbattista, M. Degemmis, N. Fanizzi N. Learning User Profiles for Content-Based Filtering in e-Commerce [R]. SSRN,2002.
[77]E. Rich. Users are Individuals:Individualizing User Models [J]. International Journal of Man-Machine Studies,1983,18(3),199-214.
[78]J. Alspector, A. Koicz, N, Karunanithi. Feature-based and Clique-based User Models for Movie Selection:A Comparative Study [J]. User Modeling and User-Adapted Interaction,1997,7(4), 279-304.
[79]J. Herlocker, J. Konstan, L. Terveen. Evaluating Collaborative Filtering Recommender Systems [J]. ACM Trans on Information Systems (TOIS),2004,22(1),5-53.
[80]R. Burke. Hybrid Systems for Personalized Recommendations [J]. Intelligent Techniques for Web Personalization, Springer Berlin Heidelberg,2005,133-152.
[81]H. S. Park, J. O. Yoo, S. B. Cho. A context-aware music recommendation system using fuzzy bayesian networks with utility theory [J]. Fuzzy Systems and Knowledge Discovery, Springer Berlin Heidelberg,2006.970-979.
[82]B. N. Miller, I. Albert, S. K. Lam. MovieLens unplugged:experiences with an occasionally connected recommender system [C]. Proceedings of the 8th international conference on Intelligent user interfaces, ACM,2003,263-266.
[83]S. Ghosh, M. Mundhe, K. Hernandez. Voting for movies:the anatomy of a recommender system [C]. Proceedings of the third annual conference on Autonomous Agents. ACM,1999, 434-435.
[84]G. Linden, B. Smith, J. York. Amazon.com recommendations:Item-to-item collaborative filtering [J]. Internet Computing, IEEE,2003,7(1),76-80.
[85]M. Deshpande, G. Karypis. Item-based top-n recommendation algorithms [J]. ACM Transactions on Information Systems (TOIS),2004,22(1),143-177.
[86]徐翔,王煦法.协同过滤算法中的相似度优化方法[J].计算机工程,2010,36(6),52-54.
[87]F. Azzedin, M. Maheswaran, Towards Trust-aware Resource Management in Grid Computing Systems [C]. Proceedings of the 2nd International Symposium on Cluster Computing and the Grid,2002,452.
[88]夏小伍,王卫平.基于信任模型的协同过滤推荐算法[J].计算机工程,2011,37(21),26-28.
[89]T. Zhou, J. Ren, M. Medo, Y. C. Zhang. Bipartite network projection and personal recommendation [J]. Physical Review E,2007,76(4),046115.
[90]T. Zhou, R. Q. Su, R. R. Liu. Accurate and diverse recommendations via eliminating redundant correlations [J], New Journal of Physics,2009,11(12),123008.
[91]J. G. Liu, T. Zhou, H. A. Che. Effects of high-order correlations on personalized recommendations for bipartite networks [J]. Physica A, Statistical Mechanics and its Applications,2010,389(4),881-886.
[92]T. Zhou, L. L. Jiang, R. Q. Su, Y. C. Zhang. Effect of initial configuration on network-based recommendation [J]. Europhysics Letters,2007 81(5),58004.
[93]Y. C. Zhang, M. Blattner, Y. K. Yu. Heat conduction process on community networks as a recommendation model [J]. Physical review letters,2007,99(15) 154301.
[94]J. G. Liu, T. Zhou, Q. Guo. Information filtering via biased heat conduction [L], Physical Review E,2011,84(3),037101.
[95]T. Zhou, Z. Kuscsik, J. G. Liu, M. Medo. Solving the apparent diversity-accuracy dilemma of recommender systems [J]. Proceedings of the National Academy of Sciences of the United States of America,2010,107(10),4511-4515.
[96]M. S. Shang, C. H. Jin, T. Zhou, Y. C. Zhang. Collaborative filtering based on multi-channel diffusion [J]. Physica A:Statistical Mechanics and its Applications,2009,388(23),4867-4871.
[97]M. S. Shang, Z. K. Zhang, T. Zhou, Y. C. Zhang. Collaborative filtering with diffusion-based similarity on tripartite graphs [J]. Physica A:Statistical Mechanics and its Applications,2009, 389(6),1259-1264.
[98]Z. K. Zhang, T. Zhou, Y. C. Zhang. Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs [J], Physica A:Statistical Mechanics and its Applications,2009, 389(1),179-186.
[99]C. X. Jia, R. R. Liu, D. Sun, B. H. Wang. A new weighting method in network-based recommendation [J]. Physica A:Statistical Mechanics and its Applications,2008,387(23), 5887-5891.
[100]X. Pan, G. S. Deng, J. G. Liu. Weighted bipartite network and personalized recommendation [J]. Physics Procedia,2010,3(5),1867-1876.
[101]J. Liu, G. S. Deng. Link prediction in a user-object network based on time-weighted resource allocation [J]. Physica A:Statistical Mechanics and its Applications 2009,388(17),3643-3650.
[102]J. G Liu, Q. Guo, Y. C. Zhang. Information filtering via weighted heat conduction algorithm [J]. Physica A:Statistical Mechanics and its Applications,2011,390(12),2414-2420.
[103]D. H. Wang, Z. Li, Z. R. Di. Bipartite producer-consumer networks and the size distribution of firms [J]. Physica A:Statistical Mechanics and its Applications,2006,363(2),359-366.
[104]M. S. Shang, Z. K. Zhang, T. Zhou, Y. C. Zhang. Collaborative filtering with diffusion-based similarity on tripartite graphs [J]. Physica A:Statistical Mechanics and its Applications, 2010,389(6),1259-1264.
[105]C. Liu, W. X. Zhou. Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm [J]. Physica A:Statistical Mechanics and its Applications,2012,391(22),5704-5711.
[106]J. C. Nacher, T. Akutsu. On the degree distribution of projected networks mapped from bipartite networks [J]. Physica A:Statistical Mechanics and its Applications,2011,390(23), 4636-4651.
[107]T. Zhou, L. L. Jiang, R. Q. Su, Y. C. Zhang. Effect of initial configuration on network-based recommendation [J]. Europhysics Letters,2008,81(5),58004.
[108]C. J. Zhang, A. Zeng. Behavior patterns of online users and the effect on information filtering [J]. Physica A:Statistical Mechanics and its Applications,2012,391(4),1822-1830.
[109]Y. L. Wang, T. Zhou, J. J. Shi, J. Wang, D. R. He. Empirical analysis of dependence between stations in Chinese railway network [J]. Physica A:Statistical Mechanics and its Applications, 2009,388(14),2949-2955.
[110]L. Lu, C. H. Jin, T. Zhou. Similarity index based on local paths for link prediction of complex networks [J]. Physical Review E,2009,80(4),046122.
[111]T. Zhou, L. Lu, Y. C. Zhang. Predicting missing links via local information. [J]. The European Physical Journal B,2009,71(4),623-630.
[112]S. Hix. A global ranking of political science departments [J]. Political Studies Review,2004, 2(3),293-313.
[113]J. Ren, T. Zhou, Y. C. Zhang. Information filtering via self-consistent refinement [J]. Europhysics Letters,2008,82(5),58007.
[114]M. G. Armentano, D. Godoy, A. Amandi. Topology-Based Recommendation of Users in Micro-B logging Communities [J]. Journal of Computer Science and Technology,2012,27(3), 624-634.
[115]F. E. Walter, S. Battiston, F. Schweitzer. A model of a trust-based recommendation system on a social network [J]. Autonomous Agents and Multi-Agent Systems,2008,16(1),57-74.
[116]P. Bonhard, M. Sasse.'Knowing me, knowing you'-Using profiles and social networking to improve recommender systems [J]. BT Technology Journal,2006,24(3),84-98.
[117]C. Wartena, R. Brussee, M. Wibbels. Using tag co-occurrence for recommendation [C]. Intelligent Systems Design and Applications, Ninth International Conference, IEEE,2009, 273-278.
[118]H. Liang, Y. Xu, Y. Li. Collaborative filtering recommender systems using tag information [C]. Web Intelligence and Intelligent Agent Technology, WI-IAT'08, IEEE/WIC/ACM International Conference,2008,3,59-62..
[119]M. S. Shang, Z. K. Zhang. Diffusion-based recommendation in collaborative tagging systems [J]. Chinese Physics Letters,2009,26(11),118903.
[120]Y. Ding, X. Li. Time weight collaborative filtering [C]. Proceedings of the 14th ACM international conference on Information and knowledge management,2005,485-492.
[121]Z. Liao, J. Wang. Forecasting model of global stock index by stochastic time effective neural network [J]. Expert Systems with Applications,2010,37(1),834-841.
[122]D. Silva, F. J. Meza, E. Varas. Estimating reference evapotranspiration (ETo) using numerical weather forecast data in central Chile [J]. Journal of Hydrology,2010,382(1),64-71.
[123]S. Mittnik, P. Zadrozny. Forecasting quarterly German GDP at monthly intervals using monthly Ifo business conditions data [J]. IFO survey data in business cycle and monetary policy analysis,2005,19-48.
[124]S. Kulkarni, I. Haidar. Forecasting model for crude oil price using artificial neural networks and commodity futures prices [J]. arXiv preprint arXiv:0906.4838,2009.
[125]L. H. Feng, J. Lu. The practical research on flood forecasting based on artificial neural networks [J]. Expert Systems with Applications,2010,37(4),2974-2977.
[126]T. H. Jordan, L. M. Jones. Reply toA Second Opinion on "Operational Earthquake Forecasting: Some Thoughts on Why and How," by Thomas H. Jordan and Lucile M. Jones,'by Stuart Crampin [J]. Seismological Research Letters,2011,82(2),231-232.
[127]J. D. Hamilton. Time series analysis [B]. Princeton:Princeton university press,1994,1(2).
[128]G Szabo, B. A. Huberman. Predicting the popularity of online content [J], Communications of the ACM,2010,53(8),80-88.
[129]H. Cheng, Y. Liu. An online public forecast model based on time series [J]. Journal of Internet Technology,2008,9(5),429-432.
[130]J. J. Cheng, Y. Liu, H. Cheng, Y. C. Zhang, X. M. Si, C. L. Zhang. Growth Trends Prediction of Online Forum Topics Based on Artificial Neural Networks [J]. Journal of Convergence Information Technology,2011,6(10),87-95.
[131]J. W. Tian, M. J. Gao. Research on Artificial Neural Networks and its Application [B]. Beijing Institute of Technology Press, China,2006.
[132]M. Campolo, A. Soldati, P. Andreussi. Artificial neural network approach to flood forecasting in the River Arno [J]. Hydrological Sciences Journal,2003,48(3),381-398,2003.
[133]K. Loukachine, N. G. Loeb. Application of an artificial neural network simulation for top-of-atmosphere radiative flux estimation from CERES [J]. Journal of Atmospheric and Oceanic Technology,2003,20(12),1749-1757.
[134]M. E. Ureyen, P. Gtlrkan. Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties [J]. Fibers and Polymers,2008,9(1),87-91.
[135]Z. X. Zhang, C. Z. Sun. Neuro-Fuzzy and Soft Computing [B]. Xi'an Jiaotong University Press, China,2000.
[136]S. F. Ding, L. Xu, C. Y. Su, H. Zhu. Using Genetic Algorithms to Optimize Artificial Neural Networks [J]. Journal of Convergence Information Technology,2010,5(8),54-62.
[137]R. P. Lippmann. An introduction to computing with neural nets [J]. ASSP Magazine, IEEE, 1987,4(2),4-22.
[138]S.I.V. Sousa, F.G. Martins, M.C.M. Alvim-Ferraz, M.C. Pereira. Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations [J]. Environmental Modelling & Software,2007,22(1),97-103.
[139]J. M. Cui. Traffic Prediction Based on Improved Neural Network [J]. Journal of Convergence Information Technology,2010,5(9),85-89.
[140]C. S. Ratana, A. E. Smith. Estimation of all-terminal network reliability using an artificial neural network [J]. Computers and Operations Research,2002,29(5),849-868.
[141]D. W. Patterson. Artificial neural networks:theory and applications [B]. Prentice Hall PTR, 1998.
[142]R. H. Nielsen. Theory of the Back Propagation Neural Network [C]. Proceedings of the International Joint Conference on Neural Networks, IEEE,1989,121-125.
[143]J. J. Cheng, Y. Liu, W. Chen. An improved growth trends prediction model of online forum topics [C]. CSCIST2011,2011,56-61.
[144]G. V. Ostir, T. Uchida. Logistic regression:A nontechnical review [J]. American journal of physical medicine & rehabilitation,2000,79(6),565-572.
[145]C. Cortes, V. Vapnik. Support vector machine. Machine learning,1995,20(3),273-297.
[146]D. W. Hosmer, S. Lemeshow. Applied logistic regression [B]. Wiley-Interscience,2004,354.
[147]S. I. Lee, H. Lee, P. Abbeel, A. Y. Ng. Efficient L-1 Regularized Logistic Regression [C]. Proceedings of the National Conference on Artificial Intelligence,2006,21(1).
[148]A. Jordan, A. On discriminative vs. generative classifiers:A comparison of logistic regression and naive bayes [J]. Advances in neural information processing systems,2002,14,841.
[149]I. Daubechies, R. DeVore, M. Fornasier, S. Gunturk. Iteratively re-weighted least squares minimization:Proof of faster than linear rate for sparse recovery [C]. Information Sciences and Systems, CISS 2008, IEEE,2008,26-29.
[150]T. P. Minka. A comparison of numerical optimizers for logistic regression [B] Unpublished draft,2003.
[151]E. W. Steyerberg, E. Frank. Jr. Harrell, G. J. Borsboom. Internal validation of predictive models:efficiency of some procedures for logistic regression analysis [J]. Journal of clinical epidemiology,2001,54(8),774-781.
[152]C. J. Lin, R. C. Weng, S. S. S. Keerthi. Trust region newton method for logistic regression [J]. The Journal of Machine Learning Research,2008,9,627-650.
[153]F. Jerome, T. Hastie, R. Tibshirani. Additive logistic regression:a statistical view of boosting (With discussion and a rejoinder by the authors) [J]. The annals of statistics,2000,28(2), 337-407.
[154]K. Koh, S. J. Kim, S. Boyd. An interior-point method for large-scale 11-regularized logistic regression [J] Journal of Machine learning research,2007 8(8),1519-1555.
[155]J. Liu, J. H. Chen, J. P. Ye. Large-scale sparse logistic regression [C]. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2009, 547-556.
[156]F. Shai, K. Scheinberg. Efficient SVM training using low-rank kernel representations [J] The Journal of Machine Learning Research,2002,2,243-264.
[157]S. S. Shevade, S. K. Shevade, S. S. Keerthi, C. Bhattacharyya, K. R. K. Murthy. Improvements to the SMO algorithm for SVM regression [C]. Neural Networks, IEEE Transactions,2000, 11(5),1188-1193.
[158]H. Zhang, A. C. Berg, M. Maire, J. Malik. SVM-KNN:Discriminative nearest neighbor classification for visual category recognition [C]. Computer Vision and Pattern Recognition, IEEE Computer Society Conference,2006,2,2126-2136.
[159]R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, C. J. Lin. LIBLINEAR:A library for large linear classification [J]. The Journal of Machine Learning Research,2008,9,1871-1874.
[160]Kai-Wei Chang, Cho-Jui Hsieh, and Chih-Jen Lin. Coordinate descent method for large-scale L2-loss linear SVM. Journal of Machine Learning Research,9:1369(1398,2008.
[161]C. H. Ho, C. J. Lin. Large-scale linear support vector regression. Technical report [J]. Journal of Machine Learning Research,2012,13,3323-3348.
[162]J. Zhu, F. Xiong, D. Piao, Y. Liu, Y. Zhang. Statistically modeling the effectiveness of disaster information in social media [C]. Global Humanitarian Technology Conference (GHTC), IEEE, 2011,431-436.
[163]尹增谦,管景峰,张晓宏,曹春梅.蒙特卡罗方法及应用[J].物理与工程,2002,12(3),45-49.
[164]M. Barthelemy, A. Barrat, R. Pastor-Satorras, A. Vespignani. Velocity and hierarchical spread of epidemic outbreaks in scale-free networks [J]. Physical Review Letter,2002,92,178701.
[165]T. Zhou, J. G. Liu, W. J. Bai. Behaviors of susceptible-infected epidemics on scale-free networks with identical infectivity [J]. Physical Review E,2006,74(5),056109.
[166]R. Pastor-Satorras, A. Vespignani. Epidemic spreading in scale-free networks [J]. Physical Review Letter,2001,86(14),3200.
[167]V. M. Eguiluz, K. Klemm. Epidemic threshold in structured scale-free networks [J]. Physical Review Letter,2002,89(10),108701.
[168]M.E.J. Newman. Spread of epidemic disease on networks [J] Physical Review E,2002,66(1), 016128.
[169]Y. Moreno, J.B. Gomez, A.F. Pacheco. Epidemic incidence in correlated complex networks [J]. Physical Review E,2003,68(3),035103.
[170]X. Z. Meng, Z. Q. Li, X. L. Wang. Dynamics of a novel nonlinear SIR model with double epidemic hypothesis and impulsive effects [J]. Nonlinear Dynamics,2010,59(3),503.
[171]周涛,傅忠谦,牛永伟,王达,曾燕,汪秉宏,周佩玲.复杂网络上传播动力学研究综述[J].自然科学进展,2005,15(5),513-518.
[172]M. Kuperman, G. Abramson. Small world effect in an epidemiological model [J]. Physical Review Letters,2001,86(13),2909-2912.
[173]N. Zekri, J. P. Clerc. Statistical and dynamical study of disease propagation in a small world network [J]. Physical Review E,2001,64(5),056115.
[174]S. Staniford, V. Paxson, N. Weaver. How to own the Internet in your spare time [C]. Proceedings of the 11th USENIX Security Symposium,2002,149-167.
[175]G. Serazzi, S. Zanero. Computer virus propagation models [J]. Performance Tools and Applications to Networked Systems,2004,26-50.
[176]C. C. Zou, D. Towsley, W. Gong. Email virus propagation modeling and analysis [J]. Technical Report TR-CSE-0304,University of Massachusetts,2003.
[177]D. Trpevski, W. K. S. Tang, L. Kocarev. Model for rumor spreading over networks [J]. Physical Review E,2010,81(5),056102.
[178]A. Sudbury. The proportion of the population never hearing a rumour [J]. Journal of applied probability,1985,22,443-446.
[179]D. H. Zanette. Dynamics of rumor propagation on small-world networks [J]. Physical Review E,2002,65(4),041908.
[180]K. Sznajd-Weron, J. Sznajd. Opinion Evolution in Closed Community [J]. International Journal of Modern Physics C,2000,11(6),1157-1165.
[181]K. Sznajd-Weron. Sznajd model and its application [J]. ACTA Physica Polonica B,2005,36(8), 2537-2547.
[182]D. Stauffer, A. O. Sousa, S. M. Oliverira. Generalization to square lattice of Sznajd sociophysics model [J]. International Journal of Modern Physics C,2000,11(6),1239-1245.
[183]A. Elgazzar. Applications of small-world networks to some socio-economic systems [J]. Physica A:Statistical Mechanics and its Applications,2003,324 (1):402-407.
[184]R. Wang, L. P. Chi, X. Cai. Opinion dynamics on complex networks with communities [J]. Chinese Physics Letters,2008,25(4),1502-1505.
[185]涂育松,李晓,邓敏艺等.一维Sznajd舆论模型相变的研究[J].广西师范大学学报(自然科学版),2005,23(3),5-8.
[186]田兴玲,刘慕仁,孔令江等.一维Sznajd舆论模型中噪声因素对演化的影响[J].广西师范大学学报(自然科学版),2006,24(1),1-4.
[187]H. J. Li, L. Z. Lin. The sznajd model with team work [J]. International Journal of Modern Physics C,2008,19(4),549-555.
[188]C. Sadowski, G. Levin. Simhash:Hash-based similarity detection [R]. Technical report, Google,2007. http://simhash.googlecode.com/svn-histoiy/r37/1runk/paper/SimHashWithBib.pdf
[189]杨建武,陈晓鸥.基于倒排索引的文本相似搜索[J],计算机工程,2005,31(5),1-3.
[190]L. Y. Lti, T. Zhou. Link prediction in complex networks:A survey [C]. Physica A:Statistical Mechanics and its Applications,2011,390(6),1150-1170.
[2]N. Pieter, H. Michiel. Mining Twitter in the Cloud:A Case Study [C]. CLOUD 2010, Miami, FL, United states, IEEE Computer Society,2010:107-114.
[3]R. Abraham, T. Martinez. Twitter:Network Properties Analysis [C]. CONIELECOMP 2010, Cholula Puebla, Mexico, IEEE Computer Society,2010:180-184.
[4]B. Kozma, A. Barrat. Consensus formation on adaptive networks [J]. Physical Review E,2008, 77,016102.
[5]汪小帆,李翔,陈关荣.复杂网络理论及其应用[M].北京：清华大学出版社,2006.
[6]R. Albert, A. L. Barabasi. Statistical Mechanics of Complex Networks [J]. Review of Modern Physics,2002,74,47-97.
[7]A. Barrat, M. Barthelemy, A. Vespignani. Modeling the evolution of weighted networks [J]. Physical Review E,2004,70,066149.
[8]R. Pastor-Satorras, A. Vazquez, A. Vespignani. Dynamical and Correlation Properties of the Internet [J]. Physical Review Letters,2001,87,258701.
[9]M. E. Newman. Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality [J]. Physical Review E,2001,64,016132.
[10]M.E. Newman. The structure of scientific collaboration networks [J]. Proceedings of the National Academy of Sciences of the United States of America,2001,98,404.
[11]H. Dietz, M. Rief. Elastic Bond Network Model for Protein Unfolding Mechanics [J]. Physical Review Letters,2008,100,098101.
[12]P. Erd6s, A. R6nyi. On the evolution of random graphs [J]. Publ. Math. Inst. Hung. Acad. Sci., 1960,5,17-60.
[13]D. J. Watts, S. H. Strogatz. Collective dynamics of 'small-world' networks [J]. Nature,1998,393, 440-442.
[14]M. E. Newman, D. J. Watts. Renormalization group analysis of the small-world network model [J]. Physics Letters A,1999,263,341-346.
[15]潘灶烽,汪小帆.一种可大范围调节聚类系数的加权无标度网络模型[J].物理学报,2006,55(8),4058-4064.
[16]A. L. Barabasi, R. Albert. Emergence of scaling in random networks [J]. Science,1999,286 (5439),509-512.
[17]李季,肖井华,蒋品群.节点数加速增长的复杂网络生长模型[J].物理学报,2006,55(8)：4051-4057.
[18]J. Jost, M. P. Joy. Evolving networks with distance preferences [J]. Physical Review E,2002,66, 036126.
[19]K. Klemm, V. E. Eguiluz. Highly clustered scale-free networks [J]. Physical Review E,2002,65, 036123.
[20]C. Tang, Y. Tang. Evolving Networks with Nonlinear Assignment of Weight [J]. Chinese Physics Letters,2006,23(1),259-262.
[21]X. P. Xu, F. Liu, W. Li. Growing Small-World Networks Based on a Modified BA Model [J]. Chinese Physics Letters,2006,23(3),750-753.
[22]S. Brin, L. Page. The anatomy of a large-scale hypertextual web search engine [J]. Computer Networks and ISDN Systems,1998,30,107-117.
[23]J. Kleinberg. Authoritative sources in a hyperlinked environment [J]. Journal of the ACM,1999, 46(5),604-632.
[24]J. L. Zhang, Y. J. Ren. Webs ranking model based on pagerank algorithm [C]. Information Science and Engineering 2010,4811-4814.
[25]S. Cheng, Y. T. Pan. PageRank, HITS and Impact Factor for Journal Ranking [C]. Computer Science and Information Engineering,2009 WRI World Congress on.2009,6,285-290.
[26]Y. Tong. Apply HITS Algorithm to Measuring Class's Authoritative Complexity [C]. WiCom 2008,1-4.
[27]A. Leavitt, E. Burchard, D. Fisher, S. Gilbert. The Influential:New Approaches for Analyzing Influence on Twitter [C]. Web Ecology Project 2009,4(2),1-18.
[28]A. D. Sarma. Ranking Mechanisms in Twitter-like Forums [C]. ACM, Web search and data mining,2010,21-30.
[29]M. Cha. Measuring User Influence in Twitter:The Million Follower Fallacy [C]. Proceedings of international AAAI Conference on Weblogs and Social,2010,10-17.
[30]R. Wang, Y. S. Jin. An Empirical Study on the Relationship between the Followers' Number and Influence of Microblogging [C]. ICEE,2010,509,2014-2017.
[31]R. Nagmoti. Ranking Approaches for Microblog Search [C]. WI-IAT,2010,170,153-157.
[32]J. S. Weng, E. P. Lim, J. Jiang, Q. He, TwitterRank:Finding Topic-Sensitive Influential Twitterers [C]. ACM,Web search and data mining,2010,261-270.
[33]Y. C. Zhang, Y. Liu, H Cheng, F Xiong, C. L. Zhang. A Method of Measuring User Influence in MicroBlog [J]. Journal of Convergence Information Technology,2011,6(10),243-250.
[34]A. Pal. Identifying Topical Authorities in Microblogs. ACM,Web search and data mining,2011, 45-54.
[35]R. X. Han. The Influence of Microblogging on Personal Public Participation [C]. Web Society (SWS),2010 IEEE 2nd Symposium, IEEE,2010,615-618.
[36]S. L. Kang, C. Zhang. Complexity Research of Massively Microblogging Based on Human Behaviors [C]. DBTA,2010,1-4.
[37]R. T. Glinton, P. Scerri, K. Sycara. Towards the understanding of information dynamics in large scale networked systems [C].12th International Conference on Information Fusion, FUSION, 2009,794-801.
[38]C. Canali, M. Colajanni, R. Lancellotti. Characteristics and evolution of content popularity and user relations in social networks [C]. Computers and Communications (ISCC),2010 IEEE Symposium,2010,750-756.
[39]F. Benevenuto, T. Rodrigues, M. Cha, V. Almeida. Characterizing user behavior in online social networks [C]. ACM SIGCOMM conference on Internet measurement conference,2009,49-62.
[40]A. Grabowski. Interpersonal interactions and human dynamics in a large social network [J]. Physica A:Statistical Mechanics and its Applications,2007,385(1),363-369.
[41]K. Naruse, M. Kubo. Lognormal distribution of BBS articles and its social and generative mechanism [C]. Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence,2006,103-112.
[42]A. Kaltenbrunner, V. Gomez, V. L6pez. Description and prediction of slashdot activity [C]. Proceedings of the 2007 Latin American Web Conference,2007,57-66.
[43]F. Ding, Y. Liu, B. Shen, H. Cheng. Modeling the reading and replying activities in a BBS social network [J]. Journal of Electronic Science Technology,2010,8(4),300-306.
[44]M. Jamali, H. Sayyadi, B. B. Hariri. A Method for Focused Crawling Using Combination of Link Structure and Content Similarity [C]. Web Intelligence,2006,753-756.
[45]J. Cho, Crawling the web:Discovery and Maintenance of Large Scaled Web Data [D]. Computer Science,2001.
[46]J. J. Tsay, C. Y. Shih, B. L. Wu, Auto crawler:An Integrated System for Automatic Topical Crawler [J]. Computer and Information Science,2005,462-467.
[47]张彦超,刘云.基于自动生成模板的Web信息抽取技术研究[J].北京交通大学学报,2009,33(5),40-45.
[48]欧健文,董守斌.模板化网页主题信息的提取方法[J],清华大学学报,2005,45(09),1743-1747.
[49]杨少华,林海略,韩燕波.针对模板生成网页的一种数据自动抽取方法[J].软件学报,2008,19(2),209-223.
[50]周明建,高济,李飞.基于本体论的Web信息抽取[J].计算机辅助设计与图形学学报,2004,16(4),535-541.
[51]F. Ashraf F, T. yzyer, R. Alhajj. Employing Clustering Techniques for Automatic Information Extraction From HTML Documents [J], Systems, Man, and Cybernetics, Part C:Applications and Reviews, IEEE Transactions,2008,38(5),660-673.
[52]张志刚,陈静,李晓明.一种html网页净化方法[J].情报学报,2004,23(4),387-393.
[53]J. Y. Wang, F. H. Lochovsky. Data-rich Section Extraction from HTML pages [C]. Proc 3rd Int Conf on Web Info SystEng(WISE.02), IEEE Computer Society Press,2002,313-322.
[54]李效东,顾毓清.基于DOM的Web信息提取[J].计算机学报,2002,25(5),526-533.
[55]李泽峰,王煜.基于RBF神经网络和关联规则的Web文本分类规则获取方法[J].图书情报工作,2006,50(10),90-92.
[56]M.X. Zhu, Z. Cai, Q. S. Cai. Automatic keywords extraction of Chinese document using small worldstructure [C]. Natural Language Processing and Knowledge Engineering,2003,438-443.
[57]罗可,林睦纲,郗东妹.数据挖掘中分类算法综述[J].计算机工程,2005,31(1),3-5.
[58]林鸿飞.基于示例的文本标题分类机制[J].计算机研究与发展,2001,38(9),1132-1136.
[59]张蓉.数据聚类技术的研究[J].计算机工程与应用.2002,16,145-147.
[60]吴艳文,胡学钢,王东波.基于特征加权的k-modes聚类应用[J].中国科技信息,2007,16,145.
[61]I. S. Dhillon, D. S. Modha. Concept Decompositions for Large Sparse Text Data Using Clustering [J]. Machine Learning,2011,42(1-2),143-175.
[62]Z. X. Huang. Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values [J]. Data Mining and Knowledge Discovery,1998,2(3),283-304.
[63]刘挺,王开铸.自动文摘的四种主要方法[J],情报学报,1999,18(1),10-19.
[64]Y. H. Zhang, J. Y. Guo, H. M. Gong, Z. S. Xue, Y. M. Zhang. Single-Document Automatic Abstracting System Based on Topic Partition [C]. Intelligent Information Technology Application Workshops,2008,280-283.
[65]L. Zhou, E. Hovy. On the Summarization of Dynamically Introduced Information:Online Discussions and Blogs [C]. Proceedings of AAA1-2006 Spring Symposium on Computational Approaches to Analyzing Weblogs,2006,237-242.
[66]H. Jiang, C. J. Peng. Automatic Text Summarization Based on Thematic Word Weight and Sentence Features [J]. Journal of South China University of Technology,2010,7,012.
[67]L. Lu, M. Medo, C. H. Yeung. Recommender systems [J]. Physics Reports,2012,519,1-49.
[68]Y. Hu, Y. Koren, C. Volinsky. Collaborative filtering for implicit feedback datasets:Data Mining [C]. ICDM '08, Eighth IEEE International Conference,2008,263-272.
[69]B. Sarwar, G. Karypis, J. Konstan. Application of dimensionality reduction in recommender system-a case study [M].DTIC Document,2000.
[70]B. Sarwar, G. Karypis, J. Konstan. Item-based collaborative filtering recommendation algorithms [C]. Proceedings of the 10th international conference on World Wide Web, ACM, 2001,285-295.
[71]D. Goldberg, D. Nichols, B. M. Oki. Using collaborative filtering to weave an information tapestry [J]. Communications of the ACM,1992,3 5(12),61-70.
[72]P. Resnick, N. Iacovou, M. Suchak. GroupLens:an open architecture for collaborative filtering of netnews [C]. ACM conference on Computer supported cooperative work,1994,175-186.
[73]H. C. Chen, A. Chen. A music recommendation system based on music data grouping and user interests [C]. Conference on Information and Knowledge Management:Proceedings of the tenth international conference on Information and knowledge management,2001,5(10),231-238.
[74]H. Thomas. Latent semantic models for collaborative filtering [J]. ACM Transactions on Information Systems (TOIS),2004,22(1),89-115.
[75]M. Balabanovic, Y. Shoham. Fab:Content-Based Collaborative Recommendation [J]. Communication of the ACM,1997,40(3),66-72..
[76]F. Abbattista, M. Degemmis, N. Fanizzi N. Learning User Profiles for Content-Based Filtering in e-Commerce [R]. SSRN,2002.
[77]E. Rich. Users are Individuals:Individualizing User Models [J]. International Journal of Man-Machine Studies,1983,18(3),199-214.
[78]J. Alspector, A. Koicz, N, Karunanithi. Feature-based and Clique-based User Models for Movie Selection:A Comparative Study [J]. User Modeling and User-Adapted Interaction,1997,7(4), 279-304.
[79]J. Herlocker, J. Konstan, L. Terveen. Evaluating Collaborative Filtering Recommender Systems [J]. ACM Trans on Information Systems (TOIS),2004,22(1),5-53.
[80]R. Burke. Hybrid Systems for Personalized Recommendations [J]. Intelligent Techniques for Web Personalization, Springer Berlin Heidelberg,2005,133-152.
[81]H. S. Park, J. O. Yoo, S. B. Cho. A context-aware music recommendation system using fuzzy bayesian networks with utility theory [J]. Fuzzy Systems and Knowledge Discovery, Springer Berlin Heidelberg,2006.970-979.
[82]B. N. Miller, I. Albert, S. K. Lam. MovieLens unplugged:experiences with an occasionally connected recommender system [C]. Proceedings of the 8th international conference on Intelligent user interfaces, ACM,2003,263-266.
[83]S. Ghosh, M. Mundhe, K. Hernandez. Voting for movies:the anatomy of a recommender system [C]. Proceedings of the third annual conference on Autonomous Agents. ACM,1999, 434-435.
[84]G. Linden, B. Smith, J. York. Amazon.com recommendations:Item-to-item collaborative filtering [J]. Internet Computing, IEEE,2003,7(1),76-80.
[85]M. Deshpande, G. Karypis. Item-based top-n recommendation algorithms [J]. ACM Transactions on Information Systems (TOIS),2004,22(1),143-177.
[86]徐翔,王煦法.协同过滤算法中的相似度优化方法[J].计算机工程,2010,36(6),52-54.
[87]F. Azzedin, M. Maheswaran, Towards Trust-aware Resource Management in Grid Computing Systems [C]. Proceedings of the 2nd International Symposium on Cluster Computing and the Grid,2002,452.
[88]夏小伍,王卫平.基于信任模型的协同过滤推荐算法[J].计算机工程,2011,37(21),26-28.
[89]T. Zhou, J. Ren, M. Medo, Y. C. Zhang. Bipartite network projection and personal recommendation [J]. Physical Review E,2007,76(4),046115.
[90]T. Zhou, R. Q. Su, R. R. Liu. Accurate and diverse recommendations via eliminating redundant correlations [J], New Journal of Physics,2009,11(12),123008.
[91]J. G. Liu, T. Zhou, H. A. Che. Effects of high-order correlations on personalized recommendations for bipartite networks [J]. Physica A, Statistical Mechanics and its Applications,2010,389(4),881-886.
[92]T. Zhou, L. L. Jiang, R. Q. Su, Y. C. Zhang. Effect of initial configuration on network-based recommendation [J]. Europhysics Letters,2007 81(5),58004.
[93]Y. C. Zhang, M. Blattner, Y. K. Yu. Heat conduction process on community networks as a recommendation model [J]. Physical review letters,2007,99(15) 154301.
[94]J. G. Liu, T. Zhou, Q. Guo. Information filtering via biased heat conduction [L], Physical Review E,2011,84(3),037101.
[95]T. Zhou, Z. Kuscsik, J. G. Liu, M. Medo. Solving the apparent diversity-accuracy dilemma of recommender systems [J]. Proceedings of the National Academy of Sciences of the United States of America,2010,107(10),4511-4515.
[96]M. S. Shang, C. H. Jin, T. Zhou, Y. C. Zhang. Collaborative filtering based on multi-channel diffusion [J]. Physica A:Statistical Mechanics and its Applications,2009,388(23),4867-4871.
[97]M. S. Shang, Z. K. Zhang, T. Zhou, Y. C. Zhang. Collaborative filtering with diffusion-based similarity on tripartite graphs [J]. Physica A:Statistical Mechanics and its Applications,2009, 389(6),1259-1264.
[98]Z. K. Zhang, T. Zhou, Y. C. Zhang. Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs [J], Physica A:Statistical Mechanics and its Applications,2009, 389(1),179-186.
[99]C. X. Jia, R. R. Liu, D. Sun, B. H. Wang. A new weighting method in network-based recommendation [J]. Physica A:Statistical Mechanics and its Applications,2008,387(23), 5887-5891.
[100]X. Pan, G. S. Deng, J. G. Liu. Weighted bipartite network and personalized recommendation [J]. Physics Procedia,2010,3(5),1867-1876.
[101]J. Liu, G. S. Deng. Link prediction in a user-object network based on time-weighted resource allocation [J]. Physica A:Statistical Mechanics and its Applications 2009,388(17),3643-3650.
[102]J. G Liu, Q. Guo, Y. C. Zhang. Information filtering via weighted heat conduction algorithm [J]. Physica A:Statistical Mechanics and its Applications,2011,390(12),2414-2420.
[103]D. H. Wang, Z. Li, Z. R. Di. Bipartite producer-consumer networks and the size distribution of firms [J]. Physica A:Statistical Mechanics and its Applications,2006,363(2),359-366.
[104]M. S. Shang, Z. K. Zhang, T. Zhou, Y. C. Zhang. Collaborative filtering with diffusion-based similarity on tripartite graphs [J]. Physica A:Statistical Mechanics and its Applications, 2010,389(6),1259-1264.
[105]C. Liu, W. X. Zhou. Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm [J]. Physica A:Statistical Mechanics and its Applications,2012,391(22),5704-5711.
[106]J. C. Nacher, T. Akutsu. On the degree distribution of projected networks mapped from bipartite networks [J]. Physica A:Statistical Mechanics and its Applications,2011,390(23), 4636-4651.
[107]T. Zhou, L. L. Jiang, R. Q. Su, Y. C. Zhang. Effect of initial configuration on network-based recommendation [J]. Europhysics Letters,2008,81(5),58004.
[108]C. J. Zhang, A. Zeng. Behavior patterns of online users and the effect on information filtering [J]. Physica A:Statistical Mechanics and its Applications,2012,391(4),1822-1830.
[109]Y. L. Wang, T. Zhou, J. J. Shi, J. Wang, D. R. He. Empirical analysis of dependence between stations in Chinese railway network [J]. Physica A:Statistical Mechanics and its Applications, 2009,388(14),2949-2955.
[110]L. Lu, C. H. Jin, T. Zhou. Similarity index based on local paths for link prediction of complex networks [J]. Physical Review E,2009,80(4),046122.
[111]T. Zhou, L. Lu, Y. C. Zhang. Predicting missing links via local information. [J]. The European Physical Journal B,2009,71(4),623-630.
[112]S. Hix. A global ranking of political science departments [J]. Political Studies Review,2004, 2(3),293-313.
[113]J. Ren, T. Zhou, Y. C. Zhang. Information filtering via self-consistent refinement [J]. Europhysics Letters,2008,82(5),58007.
[114]M. G. Armentano, D. Godoy, A. Amandi. Topology-Based Recommendation of Users in Micro-B logging Communities [J]. Journal of Computer Science and Technology,2012,27(3), 624-634.
[115]F. E. Walter, S. Battiston, F. Schweitzer. A model of a trust-based recommendation system on a social network [J]. Autonomous Agents and Multi-Agent Systems,2008,16(1),57-74.
[116]P. Bonhard, M. Sasse.'Knowing me, knowing you'-Using profiles and social networking to improve recommender systems [J]. BT Technology Journal,2006,24(3),84-98.
[117]C. Wartena, R. Brussee, M. Wibbels. Using tag co-occurrence for recommendation [C]. Intelligent Systems Design and Applications, Ninth International Conference, IEEE,2009, 273-278.
[118]H. Liang, Y. Xu, Y. Li. Collaborative filtering recommender systems using tag information [C]. Web Intelligence and Intelligent Agent Technology, WI-IAT'08, IEEE/WIC/ACM International Conference,2008,3,59-62..
[119]M. S. Shang, Z. K. Zhang. Diffusion-based recommendation in collaborative tagging systems [J]. Chinese Physics Letters,2009,26(11),118903.
[120]Y. Ding, X. Li. Time weight collaborative filtering [C]. Proceedings of the 14th ACM international conference on Information and knowledge management,2005,485-492.
[121]Z. Liao, J. Wang. Forecasting model of global stock index by stochastic time effective neural network [J]. Expert Systems with Applications,2010,37(1),834-841.
[122]D. Silva, F. J. Meza, E. Varas. Estimating reference evapotranspiration (ETo) using numerical weather forecast data in central Chile [J]. Journal of Hydrology,2010,382(1),64-71.
[123]S. Mittnik, P. Zadrozny. Forecasting quarterly German GDP at monthly intervals using monthly Ifo business conditions data [J]. IFO survey data in business cycle and monetary policy analysis,2005,19-48.
[124]S. Kulkarni, I. Haidar. Forecasting model for crude oil price using artificial neural networks and commodity futures prices [J]. arXiv preprint arXiv:0906.4838,2009.
[125]L. H. Feng, J. Lu. The practical research on flood forecasting based on artificial neural networks [J]. Expert Systems with Applications,2010,37(4),2974-2977.
[126]T. H. Jordan, L. M. Jones. Reply toA Second Opinion on "Operational Earthquake Forecasting: Some Thoughts on Why and How," by Thomas H. Jordan and Lucile M. Jones,'by Stuart Crampin [J]. Seismological Research Letters,2011,82(2),231-232.
[127]J. D. Hamilton. Time series analysis [B]. Princeton:Princeton university press,1994,1(2).
[128]G Szabo, B. A. Huberman. Predicting the popularity of online content [J], Communications of the ACM,2010,53(8),80-88.
[129]H. Cheng, Y. Liu. An online public forecast model based on time series [J]. Journal of Internet Technology,2008,9(5),429-432.
[130]J. J. Cheng, Y. Liu, H. Cheng, Y. C. Zhang, X. M. Si, C. L. Zhang. Growth Trends Prediction of Online Forum Topics Based on Artificial Neural Networks [J]. Journal of Convergence Information Technology,2011,6(10),87-95.
[131]J. W. Tian, M. J. Gao. Research on Artificial Neural Networks and its Application [B]. Beijing Institute of Technology Press, China,2006.
[132]M. Campolo, A. Soldati, P. Andreussi. Artificial neural network approach to flood forecasting in the River Arno [J]. Hydrological Sciences Journal,2003,48(3),381-398,2003.
[133]K. Loukachine, N. G. Loeb. Application of an artificial neural network simulation for top-of-atmosphere radiative flux estimation from CERES [J]. Journal of Atmospheric and Oceanic Technology,2003,20(12),1749-1757.
[134]M. E. Ureyen, P. Gtlrkan. Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties [J]. Fibers and Polymers,2008,9(1),87-91.
[135]Z. X. Zhang, C. Z. Sun. Neuro-Fuzzy and Soft Computing [B]. Xi'an Jiaotong University Press, China,2000.
[136]S. F. Ding, L. Xu, C. Y. Su, H. Zhu. Using Genetic Algorithms to Optimize Artificial Neural Networks [J]. Journal of Convergence Information Technology,2010,5(8),54-62.
[137]R. P. Lippmann. An introduction to computing with neural nets [J]. ASSP Magazine, IEEE, 1987,4(2),4-22.
[138]S.I.V. Sousa, F.G. Martins, M.C.M. Alvim-Ferraz, M.C. Pereira. Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations [J]. Environmental Modelling & Software,2007,22(1),97-103.
[139]J. M. Cui. Traffic Prediction Based on Improved Neural Network [J]. Journal of Convergence Information Technology,2010,5(9),85-89.
[140]C. S. Ratana, A. E. Smith. Estimation of all-terminal network reliability using an artificial neural network [J]. Computers and Operations Research,2002,29(5),849-868.
[141]D. W. Patterson. Artificial neural networks:theory and applications [B]. Prentice Hall PTR, 1998.
[142]R. H. Nielsen. Theory of the Back Propagation Neural Network [C]. Proceedings of the International Joint Conference on Neural Networks, IEEE,1989,121-125.
[143]J. J. Cheng, Y. Liu, W. Chen. An improved growth trends prediction model of online forum topics [C]. CSCIST2011,2011,56-61.
[144]G. V. Ostir, T. Uchida. Logistic regression:A nontechnical review [J]. American journal of physical medicine & rehabilitation,2000,79(6),565-572.
[145]C. Cortes, V. Vapnik. Support vector machine. Machine learning,1995,20(3),273-297.
[146]D. W. Hosmer, S. Lemeshow. Applied logistic regression [B]. Wiley-Interscience,2004,354.
[147]S. I. Lee, H. Lee, P. Abbeel, A. Y. Ng. Efficient L-1 Regularized Logistic Regression [C]. Proceedings of the National Conference on Artificial Intelligence,2006,21(1).
[148]A. Jordan, A. On discriminative vs. generative classifiers:A comparison of logistic regression and naive bayes [J]. Advances in neural information processing systems,2002,14,841.
[149]I. Daubechies, R. DeVore, M. Fornasier, S. Gunturk. Iteratively re-weighted least squares minimization:Proof of faster than linear rate for sparse recovery [C]. Information Sciences and Systems, CISS 2008, IEEE,2008,26-29.
[150]T. P. Minka. A comparison of numerical optimizers for logistic regression [B] Unpublished draft,2003.
[151]E. W. Steyerberg, E. Frank. Jr. Harrell, G. J. Borsboom. Internal validation of predictive models:efficiency of some procedures for logistic regression analysis [J]. Journal of clinical epidemiology,2001,54(8),774-781.
[152]C. J. Lin, R. C. Weng, S. S. S. Keerthi. Trust region newton method for logistic regression [J]. The Journal of Machine Learning Research,2008,9,627-650.
[153]F. Jerome, T. Hastie, R. Tibshirani. Additive logistic regression:a statistical view of boosting (With discussion and a rejoinder by the authors) [J]. The annals of statistics,2000,28(2), 337-407.
[154]K. Koh, S. J. Kim, S. Boyd. An interior-point method for large-scale 11-regularized logistic regression [J] Journal of Machine learning research,2007 8(8),1519-1555.
[155]J. Liu, J. H. Chen, J. P. Ye. Large-scale sparse logistic regression [C]. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM,2009, 547-556.
[156]F. Shai, K. Scheinberg. Efficient SVM training using low-rank kernel representations [J] The Journal of Machine Learning Research,2002,2,243-264.
[157]S. S. Shevade, S. K. Shevade, S. S. Keerthi, C. Bhattacharyya, K. R. K. Murthy. Improvements to the SMO algorithm for SVM regression [C]. Neural Networks, IEEE Transactions,2000, 11(5),1188-1193.
[158]H. Zhang, A. C. Berg, M. Maire, J. Malik. SVM-KNN:Discriminative nearest neighbor classification for visual category recognition [C]. Computer Vision and Pattern Recognition, IEEE Computer Society Conference,2006,2,2126-2136.
[159]R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, C. J. Lin. LIBLINEAR:A library for large linear classification [J]. The Journal of Machine Learning Research,2008,9,1871-1874.
[160]Kai-Wei Chang, Cho-Jui Hsieh, and Chih-Jen Lin. Coordinate descent method for large-scale L2-loss linear SVM. Journal of Machine Learning Research,9:1369(1398,2008.
[161]C. H. Ho, C. J. Lin. Large-scale linear support vector regression. Technical report [J]. Journal of Machine Learning Research,2012,13,3323-3348.
[162]J. Zhu, F. Xiong, D. Piao, Y. Liu, Y. Zhang. Statistically modeling the effectiveness of disaster information in social media [C]. Global Humanitarian Technology Conference (GHTC), IEEE, 2011,431-436.
[163]尹增谦,管景峰,张晓宏,曹春梅.蒙特卡罗方法及应用[J].物理与工程,2002,12(3),45-49.
[164]M. Barthelemy, A. Barrat, R. Pastor-Satorras, A. Vespignani. Velocity and hierarchical spread of epidemic outbreaks in scale-free networks [J]. Physical Review Letter,2002,92,178701.
[165]T. Zhou, J. G. Liu, W. J. Bai. Behaviors of susceptible-infected epidemics on scale-free networks with identical infectivity [J]. Physical Review E,2006,74(5),056109.
[166]R. Pastor-Satorras, A. Vespignani. Epidemic spreading in scale-free networks [J]. Physical Review Letter,2001,86(14),3200.
[167]V. M. Eguiluz, K. Klemm. Epidemic threshold in structured scale-free networks [J]. Physical Review Letter,2002,89(10),108701.
[168]M.E.J. Newman. Spread of epidemic disease on networks [J] Physical Review E,2002,66(1), 016128.
[169]Y. Moreno, J.B. Gomez, A.F. Pacheco. Epidemic incidence in correlated complex networks [J]. Physical Review E,2003,68(3),035103.
[170]X. Z. Meng, Z. Q. Li, X. L. Wang. Dynamics of a novel nonlinear SIR model with double epidemic hypothesis and impulsive effects [J]. Nonlinear Dynamics,2010,59(3),503.
[171]周涛,傅忠谦,牛永伟,王达,曾燕,汪秉宏,周佩玲.复杂网络上传播动力学研究综述[J].自然科学进展,2005,15(5),513-518.
[172]M. Kuperman, G. Abramson. Small world effect in an epidemiological model [J]. Physical Review Letters,2001,86(13),2909-2912.
[173]N. Zekri, J. P. Clerc. Statistical and dynamical study of disease propagation in a small world network [J]. Physical Review E,2001,64(5),056115.
[174]S. Staniford, V. Paxson, N. Weaver. How to own the Internet in your spare time [C]. Proceedings of the 11th USENIX Security Symposium,2002,149-167.
[175]G. Serazzi, S. Zanero. Computer virus propagation models [J]. Performance Tools and Applications to Networked Systems,2004,26-50.
[176]C. C. Zou, D. Towsley, W. Gong. Email virus propagation modeling and analysis [J]. Technical Report TR-CSE-0304,University of Massachusetts,2003.
[177]D. Trpevski, W. K. S. Tang, L. Kocarev. Model for rumor spreading over networks [J]. Physical Review E,2010,81(5),056102.
[178]A. Sudbury. The proportion of the population never hearing a rumour [J]. Journal of applied probability,1985,22,443-446.
[179]D. H. Zanette. Dynamics of rumor propagation on small-world networks [J]. Physical Review E,2002,65(4),041908.
[180]K. Sznajd-Weron, J. Sznajd. Opinion Evolution in Closed Community [J]. International Journal of Modern Physics C,2000,11(6),1157-1165.
[181]K. Sznajd-Weron. Sznajd model and its application [J]. ACTA Physica Polonica B,2005,36(8), 2537-2547.
[182]D. Stauffer, A. O. Sousa, S. M. Oliverira. Generalization to square lattice of Sznajd sociophysics model [J]. International Journal of Modern Physics C,2000,11(6),1239-1245.
[183]A. Elgazzar. Applications of small-world networks to some socio-economic systems [J]. Physica A:Statistical Mechanics and its Applications,2003,324 (1):402-407.
[184]R. Wang, L. P. Chi, X. Cai. Opinion dynamics on complex networks with communities [J]. Chinese Physics Letters,2008,25(4),1502-1505.
[185]涂育松,李晓,邓敏艺等.一维Sznajd舆论模型相变的研究[J].广西师范大学学报(自然科学版),2005,23(3),5-8.
[186]田兴玲,刘慕仁,孔令江等.一维Sznajd舆论模型中噪声因素对演化的影响[J].广西师范大学学报(自然科学版),2006,24(1),1-4.
[187]H. J. Li, L. Z. Lin. The sznajd model with team work [J]. International Journal of Modern Physics C,2008,19(4),549-555.
[188]C. Sadowski, G. Levin. Simhash:Hash-based similarity detection [R]. Technical report, Google,2007. http://simhash.googlecode.com/svn-histoiy/r37/1runk/paper/SimHashWithBib.pdf
[189]杨建武,陈晓鸥.基于倒排索引的文本相似搜索[J],计算机工程,2005,31(5),1-3.
[190]L. Y. Lti, T. Zhou. Link prediction in complex networks:A survey [C]. Physica A:Statistical Mechanics and its Applications,2011,390(6),1150-1170.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |