基于PCM聚类算法的Blog社区发现

针对传统的社区发现算法无法发现社区中的核心成员和边界成员的缺点,提出了基于PCM聚类算法的Blog社区发现算法,用来识别Blog社区的核心和边界.首先,使用随机行走的方法计算可以衡量两个Blog亲密度的对称社会距离;然后,在对称社区距离的基础上使用PCM聚类算法对Blog进行聚类,得到每个社区中的成员属于社区的概率表示.最后,通过确定相应的概率阈值,确定社区的核心和边界.实验结果表明:该算法能够获得社区中的成员属于社区的概率,根据这个概率可以确定社区中的核心成员和边界成员.     

基于新型聚类算法IncreaseK-Means的Blog相似度分析

针对现有聚类算法K-均值存在事先指定聚类类数及仿射传播存在计算复杂度偏高的缺陷,提出了一种新型的聚类算法Increase K-Means,并将其应用到Blog内容的相似度聚类分析中,较好地满足了社区发现和话题跟踪的需求.仿真结果表明:在Blog文本聚类分析中,Increase K-Means在时间上与K-Means相近,在精度上与仿射传播接近,适用于大规模网络文本的分析处理.     

Mining User Role in Social Community Application of Web 2.0

With the development of web 2.0, more and more social community applications appeared. The classical type of this kind of application is blog and facebook. The most important feature of these applications is that it is a self-media and users can post their own ideas in Interact. By using these social commtmity applications, a big social network is formed. To study the feature of social network,it is important to mine the individual information at the beginning.In this paper, we propose a User Role based method to mine the relation between the user and object thing. First, we extract the UserRole from the semantic dictionary Wordnet. Then, the feature of User Role is also mined by considering the hypemymy and hyponymy relation. Finally, we can use these features to deduce the User Role.In our experiments, we use a big corpus from TREC 2006 to test the mining performance. The experiment results show that the User Role effectively explores the feature of user.