社交网络中的社团结构挖掘

社交网络已经成为现代人们在线交流并交换信息的重要途径之一。以国内的人人网为例,大量的年轻人,尤其是学生,以此为平台,相互讨论感兴趣的话题。人与人之间因为学习关系、工作关系、共同的兴趣等诸多因素关联起来;以大学生交流为主体的社交网则更有可能因为在相同院、系、所而关联在一起,从而呈现出社团结构。该文以人人网的真实数据,使用CNM算法来验证这一假设;同时,还利用社会网络的结构知识对CNM算法作了改进,提高了社团发现的精度。所挖掘的社团结构关系还表明,高校不同院系和学科形成的社团具有各自的特点。

Community Mining in Social Network

Social Network is a new medium of exchanging information on line. Take Renren.com as an example, a myriad of young people, especially students, talk about interesting topics on this platform. People are connected for many reasons, such as studying in same college, working in same company, having interest in common. And the network nodes in Renren.com are probably joined together in groups according to the property of users department or school. In this article, the real-world network data is collected from Renren.com in the first place, and then the CNM algorithm is utilized to validate assumptions mentioned above. Based on the structure of Social Network, an improved method for discovering community structure is proposed, which outperforms the CNM in terms of accuracy. The community structure detected in the social network shows the different characteristics of each department or school in college.