社交网络专业领域社区关键技术研究与应用

发布时间：2018-02-07 11:52

本文关键词： 社交网络社区发现话题模型分布式计算领域专家　出处：《北京邮电大学》2015年硕士论文　论文类型：学位论文

【摘要】：近年来,社交网络服务迅猛发展,用户人数呈爆炸式增长。通过社交网络服务,人们除了进行日常的社交行为,则更多的是将其当作公共媒体平台。调查发现,除了跟好友保持联系之外,人们使用社交网络大多是用来获取专业的知识分享以及跟踪自己感兴趣的事件或话题。社交网络中,人们的交互有明显的社区性,相同社区内的用户多具有相同兴趣或关注点并交流密切,不同社区通过关联节点进行连接。同时,由于社交网络的用户众多,每天都会产生成千上万的信息,对于个人来说,很难有效的从海量的数据中找到自己所关注的内容,因而我们有必要研究合适的方法,来帮助用户更加高效的使用社交网络。针对上述背景,本文主要研究了社交网络专业领域社区发现问题和专业领域用户社区话题监测问题。文章首先建立了社交网络专业领域社区发现模型,该模型针对用户在社交网络上对专业领域知识的需求,在充分利用社交网络数据信息的基础上,提出了能够准确识别专业领域专家用户的专业领域专家用户界定算法。在识别出的专家用户群基础上,完成了专家用户社交网络的构建及连接强度的评估,并提出了基于用户连接强度的社区划分算法。然后,本文构建了专业领域用户社区话题监测模型,该模型针对用户面对专业领域专家社区中产生的海量数据无法有效的获知其所讨论话题的问题,在充分分析社交网络数据特征及话题分布特征的基础上,提出了有监督的层次狄利克雷分配算法,并给出了分布式解决方案,从而可以高效的监测专业领域用户社区中的热门话题。经过在真实数据的验证表明,上述两个模型相比现有的解决方案,都具有更好的性能优势。最后,基于本文研究的社交网络专业领域社区发现模型和专业领域用户社区话题监测模型,构建了社交网络专业领域社区话题监测系统。文章对该系统的整体架构、各模块设计、开发环境与运行平台、系统的运行结果以及性能分析做了详细的介绍。
[Abstract]:In recent years, social networking services have grown rapidly and the number of users has exploded. Through social networking services, people use them more as a public media platform than in their daily social activities. In addition to keeping in touch with friends, most people use social networks to gain professional knowledge sharing and track events or topics of interest to them. Most users in the same community share the same interests or concerns and communicate closely. Different communities connect through connected nodes. At the same time, because of the large number of users on social networks, thousands of messages are generated every day. It is difficult to find out what we are concerned about from the massive data, so it is necessary to study appropriate methods to help users to use social network more efficiently. In view of the above background, this paper mainly studies the social network domain community discovery problem and the specialized domain user community topic monitoring question. Firstly, the paper establishes the social network specialized domain community discovery model. This model aims at the needs of users' knowledge of professional domain on social network, and makes full use of social network data and information. Based on the expert user group identified, the construction of the social network and the evaluation of the connection intensity of the expert user are completed. A community partition algorithm based on user connection strength is proposed. Then, a model of user community topic monitoring in professional field is constructed in this paper. This model aims at the problem that users can not effectively know the topic they are discussing in the face of the massive data generated in the professional domain expert community, and based on the analysis of the social network data characteristics and topic distribution features, A supervised hierarchical Delikley assignment algorithm is proposed, and a distributed solution is given, which can efficiently monitor the hot topics in the user community in the professional field. The above two models have better performance advantages than the existing solutions. Finally, based on the social network domain community discovery model and the professional domain user community topic monitoring model, a social network professional domain community topic monitoring system is constructed. Each module design, development environment and running platform, system running results and performance analysis are introduced in detail.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP393.09

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