社会网络的社团结构发现与动态特性研究
摘要
随着互联网与信息化技术的迅速发展,社会网络已逐渐成为人们生活中不可或缺的一部分。通过对社会网络上残留的用户数字轨迹进行分析挖掘,可以增进我们对社会网络上消息传播的认识,从而促进有益信息在社会网络上更好地传播,并能对虚假、垃圾、谣言信息的爆发式传播进行及时预警。
目前社会网络研究的难点在于最有价值的信息往往隐藏在海量的数据之中。本文从两个不同的角度出发来讨论如何从海量的数据中找出最有价值的信息这一重要问题。
在社会网络的社团发现方面,本文提出了一种新的社团结构发现算法,旨在将海量的用户连接关系简化为社团与社团之间的连接关系,以及各个社团内部的关系。这样就能很大程度上减少每次需要处理的数据量,对海量数据的分析能起到化繁为简的作用。由于本算法利用到了社会网络的局部特征,所以有着较低的算法复杂度,可以用于处理海量的社会网络连接数据。和目前性能最优的社团发现算法相比,本算法在质量上和最优算法相当,而在速度上要优于目前最优的算法。
在社会网络动态特性研究方面,本文从用户在微博上转发信息这一行为中,挖掘出了每个用户的个人兴趣。在此基础上,提出了一个对每个用户收到的微博进行个性化重新排序的算法。通过对微博的重新排序,每个用户最可能感兴趣的微博会被排在最前面,而那些没有信息量、含垃圾广告等的微博会被排在最后。本微博重排算法起到了对用户微博数据过滤的作用,这样用户就能从海量的信息中,有效地挑选出自己真正需要的信息。在真实用户数据集上的测试显示,本微博重排算法弥补了目前微博系统在内容呈现上的不足。和微博默认的排序相比,本算法在排序性能上至少提升了30%。
All kinds of social networks play a more and more important role in ourdaily lives. By analyzing users' behaviors in the social networks, we can gainthe insight of the way a message propagating in the social network and get toknow how to promote new ideas and prevent spams and rumors fromepidemic spreading.
The study of the social network currently is troubled by the problem thatthe amount of the social network data is tremendous and the most valuableinformation is concealed in the whole dataset. In this work we tackle thisproblem from two aspects.
For the community detection section: we have proposed a hierarchicaldiffusion method to detect the community structure from very large socialnetworks. By using the network of communities instead of the network ofpeople, we can reduce the dimension of the social network greatly. Ourcommunity detection algorithm is based on the local structure, so it's veryefficient. Tests on both classical and synthetic benchmarks show that ouralgorithm is comparable to state of the art community detection algorithms inboth computational complexity and accuracy.
For the dynamic properties section: we infer users' interests from theirbehaviors in social networks. Then we present a supervised learning methodfor personalized tweets reordering based on users' interests. Twitter displaysthe tweets a user received in a reversed chronological order, which is notalways the best choice because many informative or relevant tweets might beflooded or displayed at the bottom due to some nonsense buzzes. Throughexploring a rich set of social and personalized features, we model the relevance of tweets by minimizing the pairwise loss of relevant andnon relevant tweets. The tweets are then reordered according to the predictedrelevance scores from the learned model. Experimental results with realtwitter user activities demonstrated the effectiveness of our method. The newmethod achieved above 30% accuracy gain compared with the defaultordering in twitter based on time.
目前社会网络研究的难点在于最有价值的信息往往隐藏在海量的数据之中。本文从两个不同的角度出发来讨论如何从海量的数据中找出最有价值的信息这一重要问题。
在社会网络的社团发现方面,本文提出了一种新的社团结构发现算法,旨在将海量的用户连接关系简化为社团与社团之间的连接关系,以及各个社团内部的关系。这样就能很大程度上减少每次需要处理的数据量,对海量数据的分析能起到化繁为简的作用。由于本算法利用到了社会网络的局部特征,所以有着较低的算法复杂度,可以用于处理海量的社会网络连接数据。和目前性能最优的社团发现算法相比,本算法在质量上和最优算法相当,而在速度上要优于目前最优的算法。
在社会网络动态特性研究方面,本文从用户在微博上转发信息这一行为中,挖掘出了每个用户的个人兴趣。在此基础上,提出了一个对每个用户收到的微博进行个性化重新排序的算法。通过对微博的重新排序,每个用户最可能感兴趣的微博会被排在最前面,而那些没有信息量、含垃圾广告等的微博会被排在最后。本微博重排算法起到了对用户微博数据过滤的作用,这样用户就能从海量的信息中,有效地挑选出自己真正需要的信息。在真实用户数据集上的测试显示,本微博重排算法弥补了目前微博系统在内容呈现上的不足。和微博默认的排序相比,本算法在排序性能上至少提升了30%。
All kinds of social networks play a more and more important role in ourdaily lives. By analyzing users' behaviors in the social networks, we can gainthe insight of the way a message propagating in the social network and get toknow how to promote new ideas and prevent spams and rumors fromepidemic spreading.
The study of the social network currently is troubled by the problem thatthe amount of the social network data is tremendous and the most valuableinformation is concealed in the whole dataset. In this work we tackle thisproblem from two aspects.
For the community detection section: we have proposed a hierarchicaldiffusion method to detect the community structure from very large socialnetworks. By using the network of communities instead of the network ofpeople, we can reduce the dimension of the social network greatly. Ourcommunity detection algorithm is based on the local structure, so it's veryefficient. Tests on both classical and synthetic benchmarks show that ouralgorithm is comparable to state of the art community detection algorithms inboth computational complexity and accuracy.
For the dynamic properties section: we infer users' interests from theirbehaviors in social networks. Then we present a supervised learning methodfor personalized tweets reordering based on users' interests. Twitter displaysthe tweets a user received in a reversed chronological order, which is notalways the best choice because many informative or relevant tweets might beflooded or displayed at the bottom due to some nonsense buzzes. Throughexploring a rich set of social and personalized features, we model the relevance of tweets by minimizing the pairwise loss of relevant andnon relevant tweets. The tweets are then reordered according to the predictedrelevance scores from the learned model. Experimental results with realtwitter user activities demonstrated the effectiveness of our method. The newmethod achieved above 30% accuracy gain compared with the defaultordering in twitter based on time.
引文
[1] Scott, J. Social network analysis:Ahandbook . Thousands Oaks[M]. 2000.
[2] Kossinets, G.,Watts, D. Empirical analysis of an evolving social network[J]. Science, 2006, 311(5757): 88.
[3] Eckmann, J.,Moses, E.,Sergi, D. Entropy of dialogues creates coherent structures in e mail traffic[J].Proceedings of the National Academy of Sciences of the United States of America, 2004, 101 (40):14333.
[4] Ebel, H.,Mielsch, L.,Bornholdt, S. Scale free topology of e mail networks[J]. Physical Review E,2002, 66 (3): 35103.
[5] Tyler, J.,Wilkinson, D.,Huberman, B. Email as spectroscopy: Automated discovery of communitystructure within organizations[A]. In Communities and Technologies[C], 2003.
[6] Onnela, J.,Saram ki, J.,Hyv nen, J., etc. Structure and tie strengths in mobile communicationnetworks[J]. Proceedings of the NationalAcademy of Sciences, 2007, 104 (18): 7332.
[7] Eagle, N.,Pentland, A.,Lazer, D. Inferring social network structure using mobile phone data[J].PNAS, 2007.
[8] Seshadri, M.,Machiraju, S.,Sridharan, A., etc. Mobile call graphs: beyond power law and lognormaldistributions[A]. In ACM SIGKDD International Conference on Knowledge Discovery and DataMining (KDD)[C], 2008; 596 604.
[9] Leskovec, J.,Horvitz, E. Planetary scale views on a large instant messaging network[A]. InInternational World Wide Web Conference (WWW)[C], 2008.
[10] Girvan, M.,Newman, M. Community structure in social and biological networks[J]. Proceedings ofthe NationalAcademy of Sciences, 2002, 99 (12): 7821.
[11] G?tz, M.,Leskovec, J.,McGlohon, M., etc. Modeling blog dynamics[A]. In AAAI Conference onWeblogs and Social Media[C], 2009.
[12] Adar, E.,Zhang, L.,Adamic, L., etc. Implicit structure and the dynamics of blogspace[A]. InWorkshop on the Weblogging Ecosystem[C], 2004.
[13] Adar, E.,Adamic, L. Tracking Information Epidemics in Blogspace[A]. In Web Intelligence[C],2005; 214.
[14] Java, A.,Song, X.,Finin, T., etc. Why we twitter: understanding microblogging usage andcommunities[A]. In Proceedings of the 9th WebKDD and 1st SNA KDD[C], 2007; 56 65.
[15] Huberman, B.,Romero, D.,Wu, F. Social networks that matter: Twitter under the microscope[J].First Monday, 2008, 14 (1).
[16] Cameron Marlow, L. B., Tom Lento, Itamar Rosenn. Maintained relationships on facebook.http://overstated.net/2009/03/09/maintained relationships on facebook
[17] Travers, J.,Milgram, S. An experimental study of the small world problem[J]. Sociometry, 1969, 32(4): 425 443.
[18] Barabasi, A.,Albert, R. Emergence of scaling in random networks[J]. Science, 1999, 286 (5439):509.
[19] Granovetter, M. Getting a job:Astudy of contacts and careers[M]. Univ of Chicago Pr, 1995.
[20] Newman, M. Detecting community structure in networks[J]. The European Physical Journal B,2004, 38 (2): 321 330.
[21] Clauset, A.,Newman, M.,Moore, C. Finding community structure in very large networks[J].Physical Review E, 2004, 70 (6): 66111.
[22] Palla, G.,Derényi, I.,Farkas, I., etc. Uncovering the overlapping community structure of complexnetworks in nature and society[J]. Nature, 2005, 435 (7043): 814 818.
[23] Guimera, R.,Danon, L.,Diaz Guilera, A., etc. Self similar community structure in a network ofhuman interactions[J]. Physical Review E, 2003, 68 (6): 65103.
[24] Gleiser, P.,Danon, L. Community structure in jazz[J].Arxiv preprint cond mat/0307434, 2003.
[25] Erd?s, P.,Rényi, A. On the evolution of random graphs[J]. Publications of tke MatkemaficalInsfifufe of the HungarianAcademy of Sciences, 1960, 5.
[26] Bianconi, G.,Barabási, A. Competition and multiscaling in evolving networks[J]. EPL(EurophysicsLetters), 2001, 54 436 442.
[27] Flaxman, A.,Frieze, A.,Vera, J. A geometric preferential attachment model of networks II[J].Internet Mathematics, 2007, 4 (1): 87 111.
[28] Kumar, R.,Raghavan, P.,Rajagopalan, S., etc. Stochastic models for the web graph[A]. In AnnualSymposium On Foundations Of Computer Science[C], 2000; 57 65.
[29] Albert, R.,Barabási, A. Statistical mechanics of complex networks[J]. Reviews of modern physics,2002, 74 (1): 47 97.
[30] Chakrabarti, D.,Faloutsos, C. Graph mining: Laws, generators, and algorithms[J]. ACM ComputingSurveys (CSUR), 2006, 38 (1): 2.
[31] Christakis, N.,Fowler, J. The spread of obesity in a large social network over 32 years[J]. NewEngland Journal of Medicine, 2007, 357 (4): 370.
[32] Fowler, J.,Christakis, N. Dynamic spread of happiness in a large social network: longitudinalanalysis over 20 years in the Framingham Heart Study[J]. British Medical Journal, 2008, 337 (dec04 2):a2338.
[33] Anderson, R.,Oxford, R. Infectious diseases of humans: dynamics and control[J]. AUSTRALIANJOURNALOF PUBLIC HEALTH, 1992, 16 (2).
[34] Coleman, J.,Katz, E.,Menzel, H. The diffusion of an innovation among physicians[J]. Sociometry,1957, 20 (4): 253 270.
[35] Wang, P.,Gonzalez, M.,Hidalgo, C., etc. Understanding the spreading patterns of mobile phoneviruses[J]. Science, 2009, 324 (5930): 1071.
[36] Madan, A.,Pentland, A. Modeling Social Diffusion Phenomena using Reality Mining[A]. In AAAISpring Symposium[C], PaloAlto, CA, 2009.
[37] Friggeri, A.,Cointet, J.,Latapy, M. A Real World Spreading Experiment in the Blogosphere[J].2009.
[38] Leskovec, J.,Adamic, L.,Huberman, B. The dynamics of viral marketing[J]. ACM Transactions onthe Web (TWEB), 2007, 1 (1): 5.
[39] Danon, L.,Díaz Guilera, A.,Duch, J., etc. Comparing community structure identification[J]. Journalof Statistical Mechanics: Theory and Experiment, 2005, 2005 P09008.
[40] Kuncheva, L.,Hadjitodorov, S. Using diversity in cluster ensembles[A]. In InternationalConference on Systems, Man and Cybernetics[C], 2004; 1214 1219.
[41] Lancichinetti, A.,Fortunato, S.,Kertész, J. Detecting the overlapping and hierarchical communitystructure in complex networks[J]. New Journal of Physics, 2009, 11 033015.
[42] Newman, M.,Girvan, M. Finding and evaluating community structure in networks[J]. PhysicalReview E, 2004, 69 (2): 26113.
[43] Porter, M.,Onnela, J.,Mucha, P. Communities in networks[J]. Notices of the AmericanMathematical Society, 2009, 56 (9): 1082 1097.
[44] Brandes, U.,Delling, D.,Gaertler, M. On modularity clustering[J]. IEEE Transactions onKnowledge and Data Engineering, 2007, 172 188.
[45] Newman, M. Finding community structure in networks using the eigenvectors of matrices[J].Physical Review E, 2006, 74 (3): 36104.
[46] Guimera, R.,Amaral, L. Functional cartography of complex metabolic networks[J]. Nature, 2005,433 (7028): 895 900.
[47] Duch, J.,Arenas, A. Community detection in complex networks using extremal optimization[J].Physical Review E, 2005, 72 (2): 27104.
[48] Fortunato, S.,Barthélemy, M. Resolution limit in community detection[J]. Proceedings of theNationalAcademy of Sciences, 2007, 104 (1): 36.
[49] Blondel, V.,Guillaume, J.,Lambiotte, R., etc. Fast unfolding of communities in large networks[J].Journal of Statistical Mechanics: Theory and Experiment, 2008, 2008 P10008.
[50] Rosvall, M.,Bergstrom, C. Maps of random walks on complex networks reveal communitystructure[J]. Proceedings of the NationalAcademy of Sciences, 2008, 105 (4): 1118.
[51] Lancichinetti, A.,Fortunato, S. Community detection algorithms: a comparative analysis[J].Physical Review E, 2009, 80 (5): 56117.
[52] Ravasz, E.,Somera, A.,Mongru, D., etc. Hierarchical organization of modularity in metabolicnetworks[J]. Science, 2002, 297 (5586): 1551.
[53] Radicchi, F.,Castellano, C.,Cecconi, F., etc. Defining and identifying communities in networks[J].Proceedings of the NationalAcademy of Sciences of the United States ofAmerica, 2004, 101 (9): 2658.
[54] Yong Yeol Ahn, J. P. B., Sune Lehmann. Link communities reveal multiscale complexity innetworks[J]. Nature, 2010, 466 (7307): 761 764.
[55] Fortunato, S. Community detection in graphs[J]. Physics Reports, 486 (3 5): 75 174.
[56] Morris, S. Contagion[J]. Review of Economic Studies, 2000, 67 (1): 57 78.
[57] Zachary, W. An information flow model for conflict and fission in small groups[J]. Journal ofAnthropological Research, 1977, 33 (4): 452 473.
[58] Adamic, L.,Glance, N. The political blogosphere and the 2004 US election: divided they blog[A].In Proceedings of the 3rd international workshop on Link discovery[C], 2005; 36 43.
[59] Lancichinetti, A.,Fortunato, S.,Radicchi, F. Benchmark graphs for testing community detectionalgorithms[J]. Physical Review E, 2008, 78 (4): 46110.
[60] Bollen, J.,Mao, H.,Zeng, X. Twitter mood predicts the stock market[J]. Arxiv preprintarXiv:1010.3003.
[61] Tumasjan, A.,Sprenger, T.,Sandner, P., etc. Predicting elections with Twitter: What 140 charactersreveal about political sentiment[A]. In International AAAI Conference on Weblogs and Social Media[C],Washington, DC.
[62] Ritterman, J.,Osborne, M.,Klein, E. Using prediction markets and Twitter to predict a swine flupandemic[A]. In 1st International Workshop on Mining Social Media[C], 2009.
[63]Asur, S.,Huberman, B. Predicting the future with social media[J].Arxiv preprint arXiv:1003.5699.
[64] Kelly, R. Twitter Study Reveals Interesting Results About Usage. In Twitter Study August 2009,SanAntonio,Texas: PearAnalytics: 2009.
[65] Joachims, T. Optimizing search engines using clickthrough data[A]. In Proceedings of the eighthACM SIGKDD international conference on Knowledge discovery and data mining[C], 2002; 133 142.
[66] Y. Duan, L. J., T. Qin, M. Zhou, H. Y. Shum. An Empirical Study on Learning to Rank ofTweets[A]. In Proceedings of the 23rd International Conference on Computational Linguistics[C], 2010;295 303.
[67] Page, L.,Brin, S.,Motwani, R., etc. The pagerank citation ranking: Bringing order to the web[R].1998.
[68] Kwak, H.,Lee, C.,Park, H., etc. What is Twitter, a social network or a news media?[A]. InProceedings of the 19th international conference on World wide web[C], 591 600.
[69] Chen, J.,Nairn, R.,Nelson, L., etc. Short and tweet: experiments on recommending content frominformation streams[A]. In Proceedings of the 28th international conference on Human factors incomputing systems[C], 1185 1194.
[70] Suh, B.,Hong, L.,Pirolli, P., etc. Want to be Retweeted? Large Scale Analytics on Factors ImpactingRetweet in Twitter Network[A]. In Social Computing (SocialCom), 2010 IEEE Second InternationalConference on[C], 177 184.
[71] Hofmann, T. Probabilistic latent semantic analysis[A]. In Proc. of Uncertainty in ArtificialIntelligence, UAI’99[C], 1999; 289–296.
[72] Zheng, Z.,Chen, K.,Sun, G., etc. A regression framework for learning ranking functions usingrelative relevance judgments[A]. In Proceedings of the 30th annual international ACM SIGIRconference on Research and development in information retrieval[C], 2007; 287 294.
[73] Friedman, J. Greedy function approximation: a gradient boosting machine[J]. Annals of Statistics,2001, 1189 1232.
[74] Nagmoti, R.,Teredesai, A.,De Cock, M. Ranking Approaches for Microblog Search[A]. InProceedings of 2010 IEEE/WIC/ACM International Joint Conference on Web Intelligence[C].
[2] Kossinets, G.,Watts, D. Empirical analysis of an evolving social network[J]. Science, 2006, 311(5757): 88.
[3] Eckmann, J.,Moses, E.,Sergi, D. Entropy of dialogues creates coherent structures in e mail traffic[J].Proceedings of the National Academy of Sciences of the United States of America, 2004, 101 (40):14333.
[4] Ebel, H.,Mielsch, L.,Bornholdt, S. Scale free topology of e mail networks[J]. Physical Review E,2002, 66 (3): 35103.
[5] Tyler, J.,Wilkinson, D.,Huberman, B. Email as spectroscopy: Automated discovery of communitystructure within organizations[A]. In Communities and Technologies[C], 2003.
[6] Onnela, J.,Saram ki, J.,Hyv nen, J., etc. Structure and tie strengths in mobile communicationnetworks[J]. Proceedings of the NationalAcademy of Sciences, 2007, 104 (18): 7332.
[7] Eagle, N.,Pentland, A.,Lazer, D. Inferring social network structure using mobile phone data[J].PNAS, 2007.
[8] Seshadri, M.,Machiraju, S.,Sridharan, A., etc. Mobile call graphs: beyond power law and lognormaldistributions[A]. In ACM SIGKDD International Conference on Knowledge Discovery and DataMining (KDD)[C], 2008; 596 604.
[9] Leskovec, J.,Horvitz, E. Planetary scale views on a large instant messaging network[A]. InInternational World Wide Web Conference (WWW)[C], 2008.
[10] Girvan, M.,Newman, M. Community structure in social and biological networks[J]. Proceedings ofthe NationalAcademy of Sciences, 2002, 99 (12): 7821.
[11] G?tz, M.,Leskovec, J.,McGlohon, M., etc. Modeling blog dynamics[A]. In AAAI Conference onWeblogs and Social Media[C], 2009.
[12] Adar, E.,Zhang, L.,Adamic, L., etc. Implicit structure and the dynamics of blogspace[A]. InWorkshop on the Weblogging Ecosystem[C], 2004.
[13] Adar, E.,Adamic, L. Tracking Information Epidemics in Blogspace[A]. In Web Intelligence[C],2005; 214.
[14] Java, A.,Song, X.,Finin, T., etc. Why we twitter: understanding microblogging usage andcommunities[A]. In Proceedings of the 9th WebKDD and 1st SNA KDD[C], 2007; 56 65.
[15] Huberman, B.,Romero, D.,Wu, F. Social networks that matter: Twitter under the microscope[J].First Monday, 2008, 14 (1).
[16] Cameron Marlow, L. B., Tom Lento, Itamar Rosenn. Maintained relationships on facebook.http://overstated.net/2009/03/09/maintained relationships on facebook
[17] Travers, J.,Milgram, S. An experimental study of the small world problem[J]. Sociometry, 1969, 32(4): 425 443.
[18] Barabasi, A.,Albert, R. Emergence of scaling in random networks[J]. Science, 1999, 286 (5439):509.
[19] Granovetter, M. Getting a job:Astudy of contacts and careers[M]. Univ of Chicago Pr, 1995.
[20] Newman, M. Detecting community structure in networks[J]. The European Physical Journal B,2004, 38 (2): 321 330.
[21] Clauset, A.,Newman, M.,Moore, C. Finding community structure in very large networks[J].Physical Review E, 2004, 70 (6): 66111.
[22] Palla, G.,Derényi, I.,Farkas, I., etc. Uncovering the overlapping community structure of complexnetworks in nature and society[J]. Nature, 2005, 435 (7043): 814 818.
[23] Guimera, R.,Danon, L.,Diaz Guilera, A., etc. Self similar community structure in a network ofhuman interactions[J]. Physical Review E, 2003, 68 (6): 65103.
[24] Gleiser, P.,Danon, L. Community structure in jazz[J].Arxiv preprint cond mat/0307434, 2003.
[25] Erd?s, P.,Rényi, A. On the evolution of random graphs[J]. Publications of tke MatkemaficalInsfifufe of the HungarianAcademy of Sciences, 1960, 5.
[26] Bianconi, G.,Barabási, A. Competition and multiscaling in evolving networks[J]. EPL(EurophysicsLetters), 2001, 54 436 442.
[27] Flaxman, A.,Frieze, A.,Vera, J. A geometric preferential attachment model of networks II[J].Internet Mathematics, 2007, 4 (1): 87 111.
[28] Kumar, R.,Raghavan, P.,Rajagopalan, S., etc. Stochastic models for the web graph[A]. In AnnualSymposium On Foundations Of Computer Science[C], 2000; 57 65.
[29] Albert, R.,Barabási, A. Statistical mechanics of complex networks[J]. Reviews of modern physics,2002, 74 (1): 47 97.
[30] Chakrabarti, D.,Faloutsos, C. Graph mining: Laws, generators, and algorithms[J]. ACM ComputingSurveys (CSUR), 2006, 38 (1): 2.
[31] Christakis, N.,Fowler, J. The spread of obesity in a large social network over 32 years[J]. NewEngland Journal of Medicine, 2007, 357 (4): 370.
[32] Fowler, J.,Christakis, N. Dynamic spread of happiness in a large social network: longitudinalanalysis over 20 years in the Framingham Heart Study[J]. British Medical Journal, 2008, 337 (dec04 2):a2338.
[33] Anderson, R.,Oxford, R. Infectious diseases of humans: dynamics and control[J]. AUSTRALIANJOURNALOF PUBLIC HEALTH, 1992, 16 (2).
[34] Coleman, J.,Katz, E.,Menzel, H. The diffusion of an innovation among physicians[J]. Sociometry,1957, 20 (4): 253 270.
[35] Wang, P.,Gonzalez, M.,Hidalgo, C., etc. Understanding the spreading patterns of mobile phoneviruses[J]. Science, 2009, 324 (5930): 1071.
[36] Madan, A.,Pentland, A. Modeling Social Diffusion Phenomena using Reality Mining[A]. In AAAISpring Symposium[C], PaloAlto, CA, 2009.
[37] Friggeri, A.,Cointet, J.,Latapy, M. A Real World Spreading Experiment in the Blogosphere[J].2009.
[38] Leskovec, J.,Adamic, L.,Huberman, B. The dynamics of viral marketing[J]. ACM Transactions onthe Web (TWEB), 2007, 1 (1): 5.
[39] Danon, L.,Díaz Guilera, A.,Duch, J., etc. Comparing community structure identification[J]. Journalof Statistical Mechanics: Theory and Experiment, 2005, 2005 P09008.
[40] Kuncheva, L.,Hadjitodorov, S. Using diversity in cluster ensembles[A]. In InternationalConference on Systems, Man and Cybernetics[C], 2004; 1214 1219.
[41] Lancichinetti, A.,Fortunato, S.,Kertész, J. Detecting the overlapping and hierarchical communitystructure in complex networks[J]. New Journal of Physics, 2009, 11 033015.
[42] Newman, M.,Girvan, M. Finding and evaluating community structure in networks[J]. PhysicalReview E, 2004, 69 (2): 26113.
[43] Porter, M.,Onnela, J.,Mucha, P. Communities in networks[J]. Notices of the AmericanMathematical Society, 2009, 56 (9): 1082 1097.
[44] Brandes, U.,Delling, D.,Gaertler, M. On modularity clustering[J]. IEEE Transactions onKnowledge and Data Engineering, 2007, 172 188.
[45] Newman, M. Finding community structure in networks using the eigenvectors of matrices[J].Physical Review E, 2006, 74 (3): 36104.
[46] Guimera, R.,Amaral, L. Functional cartography of complex metabolic networks[J]. Nature, 2005,433 (7028): 895 900.
[47] Duch, J.,Arenas, A. Community detection in complex networks using extremal optimization[J].Physical Review E, 2005, 72 (2): 27104.
[48] Fortunato, S.,Barthélemy, M. Resolution limit in community detection[J]. Proceedings of theNationalAcademy of Sciences, 2007, 104 (1): 36.
[49] Blondel, V.,Guillaume, J.,Lambiotte, R., etc. Fast unfolding of communities in large networks[J].Journal of Statistical Mechanics: Theory and Experiment, 2008, 2008 P10008.
[50] Rosvall, M.,Bergstrom, C. Maps of random walks on complex networks reveal communitystructure[J]. Proceedings of the NationalAcademy of Sciences, 2008, 105 (4): 1118.
[51] Lancichinetti, A.,Fortunato, S. Community detection algorithms: a comparative analysis[J].Physical Review E, 2009, 80 (5): 56117.
[52] Ravasz, E.,Somera, A.,Mongru, D., etc. Hierarchical organization of modularity in metabolicnetworks[J]. Science, 2002, 297 (5586): 1551.
[53] Radicchi, F.,Castellano, C.,Cecconi, F., etc. Defining and identifying communities in networks[J].Proceedings of the NationalAcademy of Sciences of the United States ofAmerica, 2004, 101 (9): 2658.
[54] Yong Yeol Ahn, J. P. B., Sune Lehmann. Link communities reveal multiscale complexity innetworks[J]. Nature, 2010, 466 (7307): 761 764.
[55] Fortunato, S. Community detection in graphs[J]. Physics Reports, 486 (3 5): 75 174.
[56] Morris, S. Contagion[J]. Review of Economic Studies, 2000, 67 (1): 57 78.
[57] Zachary, W. An information flow model for conflict and fission in small groups[J]. Journal ofAnthropological Research, 1977, 33 (4): 452 473.
[58] Adamic, L.,Glance, N. The political blogosphere and the 2004 US election: divided they blog[A].In Proceedings of the 3rd international workshop on Link discovery[C], 2005; 36 43.
[59] Lancichinetti, A.,Fortunato, S.,Radicchi, F. Benchmark graphs for testing community detectionalgorithms[J]. Physical Review E, 2008, 78 (4): 46110.
[60] Bollen, J.,Mao, H.,Zeng, X. Twitter mood predicts the stock market[J]. Arxiv preprintarXiv:1010.3003.
[61] Tumasjan, A.,Sprenger, T.,Sandner, P., etc. Predicting elections with Twitter: What 140 charactersreveal about political sentiment[A]. In International AAAI Conference on Weblogs and Social Media[C],Washington, DC.
[62] Ritterman, J.,Osborne, M.,Klein, E. Using prediction markets and Twitter to predict a swine flupandemic[A]. In 1st International Workshop on Mining Social Media[C], 2009.
[63]Asur, S.,Huberman, B. Predicting the future with social media[J].Arxiv preprint arXiv:1003.5699.
[64] Kelly, R. Twitter Study Reveals Interesting Results About Usage. In Twitter Study August 2009,SanAntonio,Texas: PearAnalytics: 2009.
[65] Joachims, T. Optimizing search engines using clickthrough data[A]. In Proceedings of the eighthACM SIGKDD international conference on Knowledge discovery and data mining[C], 2002; 133 142.
[66] Y. Duan, L. J., T. Qin, M. Zhou, H. Y. Shum. An Empirical Study on Learning to Rank ofTweets[A]. In Proceedings of the 23rd International Conference on Computational Linguistics[C], 2010;295 303.
[67] Page, L.,Brin, S.,Motwani, R., etc. The pagerank citation ranking: Bringing order to the web[R].1998.
[68] Kwak, H.,Lee, C.,Park, H., etc. What is Twitter, a social network or a news media?[A]. InProceedings of the 19th international conference on World wide web[C], 591 600.
[69] Chen, J.,Nairn, R.,Nelson, L., etc. Short and tweet: experiments on recommending content frominformation streams[A]. In Proceedings of the 28th international conference on Human factors incomputing systems[C], 1185 1194.
[70] Suh, B.,Hong, L.,Pirolli, P., etc. Want to be Retweeted? Large Scale Analytics on Factors ImpactingRetweet in Twitter Network[A]. In Social Computing (SocialCom), 2010 IEEE Second InternationalConference on[C], 177 184.
[71] Hofmann, T. Probabilistic latent semantic analysis[A]. In Proc. of Uncertainty in ArtificialIntelligence, UAI’99[C], 1999; 289–296.
[72] Zheng, Z.,Chen, K.,Sun, G., etc. A regression framework for learning ranking functions usingrelative relevance judgments[A]. In Proceedings of the 30th annual international ACM SIGIRconference on Research and development in information retrieval[C], 2007; 287 294.
[73] Friedman, J. Greedy function approximation: a gradient boosting machine[J]. Annals of Statistics,2001, 1189 1232.
[74] Nagmoti, R.,Teredesai, A.,De Cock, M. Ranking Approaches for Microblog Search[A]. InProceedings of 2010 IEEE/WIC/ACM International Joint Conference on Web Intelligence[C].
© 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 |