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工程科学与技术:2017,49(Z2):195-202
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面向多源社交网络的社团结构特征研究(研究生论坛)
(1.四川大学计算机学院;2.四川大学网络空间安全研究院)
Research on Community Characteristics of Multi-Source Social Network
(1.Cybersecurity Research Institute, Sichuan University;2.College of Computer Science, Sichuan University)
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投稿时间:2016-09-03    修订日期:2016-12-31
中文摘要: 为了研究社交网络社团结构对舆情传播的影响,本文对比分析了多源社交网络的社团结构特性及传播特性,并且利用COPRA算法和LFM算法进行了社交网络重叠社团研究,提出一种基于节点度过滤的LFM改进方法——NF-LFM算法。该算法对好友关系网络中节点度小于某一阈值的节点进行过滤,再对剩下的好友关系网络进行社团划分。实验结果表明:1)人人网、QQ空间、新浪微博都具有明显的社团结构特性,其中人人网和QQ空间的社团结构特性强于新浪微博;2)在不考虑社交网络用户活跃度的情况下,舆情信息在人人网上扩散范围最广,新浪微博次之,QQ空间扩散较慢。本文提出的改进方法能解决现有算法社团划分结果分辨率低的问题,且有效弥补了LFM算法在大规模社团发现时陷入无限的迭代过程而导致时间复杂度高的缺点,将其应用于经典数据集中也符合理论预期结果。本文的研究结果将有助于进一步理解和认识社交网络社团结构对舆情传播的影响,同时对于网络群体事件发现和舆情监控及引导等具有重要意义。
Abstract:In order to analyze the impact of social networks on the spread of public opinion, the multi-source network community structure characteristics and its propagation characteristics was studied. Furthermore, the overlapping community was discussed using COPRA algorithm and LFM algorithm in social network and the improved algorithm of LFM based on filtering node degree——NF-LFM algorithm was proposed. In this algorithm, the node whose node degree is less than a certain threshold in the friend relation network was filtered, and then the rest of the network was divided into community structure. The results showed that: 1) The social networks such as Renren, Qzone and Microblog have obvious characteristics of the community structure, of which Renren and Qzone is stronger than Microblog. 2) Without taking into account the user activity, the diffusion range of public opinion information in Renren is larger than the others. The proposed method can not only solve the resolution problem of the existing algorithm in the community division results effectively, but also make up for the shortcomings of the LFM algorithm, which is caught in an infinite iterative process and leads to high time complexity. It is also in line with expected results when is applied to the classical data set. The results of this paper can help researchers to understand the impact on public opinion spread in community. It also has important significance for the discovery of network group incidents and guidance of public sentiment.
文章编号:201600945     中图分类号:    文献标志码:
基金项目:国家科技支撑计划资助项目(2012BAH18B05:新媒体资源监管关键技术原型系统研究);国家自然科学基金项目(61272447:基于动态多维特征的网络行为模型研究);四川大学青年教师启动基金(2015SCU11079:移动互联网中P2P流媒体系统的内容污染建模研究)
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李敏毓,陈兴蜀,尹雅丽,王海舟,王文贤.面向多源社交网络的社团结构特征研究(研究生论坛)[J].工程科学与技术,2017,49(Z2):195-202.
李敏毓,Chen Xingshu,Wang Haizhou.Research on Community Characteristics of Multi-Source Social Network[J].Advanced Engineering Sciences,2017,49(Z2):195-202.