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在线社交网络挖掘综述 被引量:9

A Survey on Online Social Network Mining
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摘要 介绍了在线社交网络挖掘产生的背景以及不同学科领域研究的侧重点,并对在线社交网络挖掘所涉及的用户分类、社区发现、观点挖掘、情感分析、信息传播、社会化推荐以及可视化分析等代表性研究话题的现状进行了详细论述,重点归纳了每一个话题所涉及的关键问题和代表性解决方法.分析和讨论了在线社交网络的迅速发展给在线社交网络挖掘领域所带来的新问题和新挑战,最后指出了该领域的发展前景. Online social nelwork (OSN) mining has become an emerging research field. This paper introduces the background of OSN mining and the research emphasis of different related disciplines. The research status of the hottest topics in OSN mining are discussed, including topics of user classification, community discovery, opinion mining, sentiment analysis, information diffusion, social recommendation and visualization analysis. Key problems and representative methods that every topic refers to are chiefly detailed. This paper also discusses the existing problems and challenges in OSN mining. Finally, this paper points out the development prospect of OSN mining.
出处 《武汉大学学报(理学版)》 CAS CSCD 北大核心 2014年第3期189-200,共12页 Journal of Wuhan University:Natural Science Edition
基金 国家高技术研究发展计划(863计划)项目(2013AA01A212) 国家自然科学基金资助项目(60970044 61272067 61370178) 国家科技支撑计划项目(2012BAH27F05) 广东省自然基金团队研究项目(S2012030006242) 广东省科技计划项目(2012A080104019 2011B080100031) 广东省高校优秀青年创新人才培养计划项目(2012LYM_0077)
关键词 社交网络挖掘 用户分类 社区发现 观点挖掘 情感分析 信息传播 社会化推荐 可视化分析 social network mining user classification community discovery opinion mining sentiment analysis information diffusion social recommendation visualization analysis
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  • 1Wikipedia.Social network service[EB/OL].[2013-12-25].http://en.wikiPedia.org/wiki/Social_network_service.
  • 2Boyd D M,Ellison N B.Social network sites:Definition,history,and scholarship [J].Journal of Computer-Mediated Communication,2008,13(1):210-230.
  • 3King I.Introduction to social computing [C]//Proceedings of 15th International Conference on Database Systems for Advanced Applications,Berlin,Heidelberg:Springer-Verlag,2010:482-484.
  • 4Amazon.Alexa [EB/OL].[2013-12-20].http://www.alexa.com/.
  • 5Alexa.Top Sites[EB/OL].[2013-12-23].http://www.alexa.com/topsites.
  • 6Ahn Y v,Bagrow J p,Lehmann S.Link communities reveal multi scale complexity in networks[J].Nature,2010,466:761-764.
  • 7Aral S,Walker D.Identifying influential and susceptible members of social networks [J].Science,2012,337(6092):337-341.
  • 8Arkaitz Z,Christian K,Markus S.Tags vs Shelves:from social tagg-ing to social classification[C]//Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia,New York:ACM,2011:93-102.
  • 9Delip R,David y,Abhishek S,et al.Classifying latent user attributes in twitter[C]//Proceedings of the 2nd International Workshop on Search and Mining User-generated Contents,N ew York:ACM,2010:37-44.
  • 10Pennacchiotti M,Popescu A M.A machine learning approach to twitter user classification [C]//Proceedings of the 5th International AAAI Conference on Weblogs and Social Media,Menlo Park,California:AAAI Press,2011:281-288.

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