面向多源社交网络的社团结构特征研究

Research on Community Characteristics of Multi-source Social Network

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。该算法先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。研究发现: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。

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,named as NF-LFM algorithm,was proposed. In this algorithm,the node with node degree 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 the social networks such as Renren,Qzone and Microblog had obvious characteristics of the community structure,of which Renren and Qzone was stronger than Microblog. Without taking into account the user activity,the diffusion range of public opinion information in Renren was larger than the others.The proposed method could 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 was caught in an infinite iterative process and leads to high time complexity. It was also in line with expected results when it was applied to the classical data set. The results can help researchers 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.