基于结构的社会网络分析

Social Network Analysis Based on Structure

互联网的发展和社交网站的流行为研究社会网络提供了大规模的实验平台.主要使用DBLP和Facebook数据集构建网络,采取角色连接轮廓方法从结构上进行划分,发现它们属于外围串类型;验证了社会网络的一些统计性质,比如无标度分布、稠化定律和直径缩减等;发现社会网络中存在紧密连接且直径较小的核心结构,规模中等的社区主要呈现星型结构;基于事件框架研究了社会网络中社区结构的进化,发现社区间的融合很大程度上取决于社区间直接连接的节点所构成网络的聚类系数,而社区的分裂则与该社区的聚类系数相关.

The development of Internet and the popularity of social sites provide the large-scale experimental platform for researching the statistical properties and structure evolution of social networks.This paper mainly uses DBLP and Facebook datasets and built the social networks.We classify these networks by using role-to-role connectivity profiles and found that they belong to stringy-periphery class.We confirm that they have these properties,such as free-scale distribution,densification law and shrinking diameter.We discover there is a small core with high connectivity in social networks,and observed that many middle-scale communities are composed of stars.We research the evolution of community structure based on event framework and revealed that the community merge depends largely on the clustering coefficient of the graph composed of nodes which are directly connected between communities and the community split is related to its clustering coefficient.