在线社交网络挖掘与搜索技术研究
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摘要
随着在线社交网络的蓬勃发展,传统的数据挖掘的和搜索方法已经不能完全适用于Web 2.0时代的社交网络。社交网络具有社交关系复杂、数据量大、动态更新、数据多模态等特点,给数据挖掘和搜索的研究来了巨大的挑战。因此,研究基于社交网络挖掘和搜索的新方法成为学术界和工业界的一项新任务。文章全面分析了社交网络发展的基本情况和存在的问题,阐述了社交网络结构建模、信息传播机制、社区发现、情感分析、事件监测及社交网络搜索排序技术的主要研究工作,并基于已有研究工作对社交网络挖掘和网络搜索技术进行了分析和展望。
With the vigorous development of online social networks, the traditional technologies of data mining andsearching cannot solve the problems of social networks in the Web 2.0 era. Social networks, accompanied by com-plex social relationships, large amounts of data, dynamic updates, multimodal data, etc. have brought great chal-lenge to the study of data mining and searching. Therefore, the research of novel algorithms of social network miningand searching has become a new task in both academia and industry. This paper summarized the basic situation andproblems of social networks, and analyzed structural modeling techniques, information transmission mechanisms,community detection, sentiment analysis, event detection and search ranking techniques of social networks. Basedon the analysis of previous researches, the prospect of social network data mining and search technologies was fore-casted in this paper.
With the vigorous development of online social networks, the traditional technologies of data mining andsearching cannot solve the problems of social networks in the Web 2.0 era. Social networks, accompanied by com-plex social relationships, large amounts of data, dynamic updates, multimodal data, etc. have brought great chal-lenge to the study of data mining and searching. Therefore, the research of novel algorithms of social network miningand searching has become a new task in both academia and industry. This paper summarized the basic situation andproblems of social networks, and analyzed structural modeling techniques, information transmission mechanisms,community detection, sentiment analysis, event detection and search ranking techniques of social networks. Basedon the analysis of previous researches, the prospect of social network data mining and search technologies was fore-casted in this paper.
引文
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