一种基于拉普拉斯矩阵的在线社会网络社区发现算法

Web媒体被公认为继报纸、广播、电视之后的＂第四媒体＂。而Web2.0的迅速普及,又使当今的Web媒体呈现了一种＂自媒体＂形式,即每个用户既是信息的接受者,也是信息发布者和信息转发者,因此,在当今的Web上形成了在线社会网络。研究表明在线社会网络呈现出一种很强的＂模块性＂（＂社区性＂）,因此,在在线社会网络中,社区发现一直是一个研究热点,即如何设计算法以发现大规模社会网络中的社区结构。文章提出了一种基于拉普拉斯矩阵的在线社会网络社区发现算法,该算法将在线社会网络转换成以拉普拉斯矩阵形式表现,通过计算该矩阵的谱并利用其性质发现社会网络上的社区结构。文章同时针对人造数据集与真实数据集进行了实验,实验结果表明本算法能够有效的发现社会网络中的社区结构。

Community Discovery Algorithm Based on Laplacian Matrix in Social Networks

Web media is generally acknowledged as ＂the fourth media＂ after the newspaper,broadcast and TV.And as Web 2.0 prevails over the internet,the web media has a form called ＂self-media＂,which means that every individual is a receiver,also it is a publisher and a forwarder at the same time.Therefore,online social networks have been formed.It has been shown that most of these networks exhibit strong modular nature（or community structure）.In this paper,a community discovery algorithm is proposed based on Laplacian matrix,this algorithm convert a social network structure into Laplacian matrix,calculate its spectral and using the properties to discover the community structure from the social network.A lot of experiments have been done on real word dataset,and the experimental results show that the algorithm can discover the community structure effectively.