欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
The studies show that numerous complex networks have clustering effect.It is an indispensable step to identify node clusters in network,namely community,in which nodes are closely related,and in many applications such as identification of ringleaders in anti-criminal and anti-terrorist network,efficient storage of data in Wireless Sensor Network(WSN).At present,most of community identification methods still require the specifications of the number or the scale of community by user and still can not handle overlapping nodes.In an attempt to solve these problems,a network community identification method based on utility value is proposed,which is a function of each node’s clustering coefficient and degree.This method makes use of individual-centered theory for reference and can automatically determine the number of communities.In addition,this method is an overlapping community identification method in nature.It is shown through contrastive experiments that this method is more efficient than other methods based on individual-centered theory when they control the same amount of information.Finally,a research direction is proposed for network community identification method based on the individual-centered theory.
英文摘要：
The studies show that numerous complex networks have clustering effect. It is an indispensable step to identify node clusters in network, namely community, in which nodes are closely related, and in many appli- cations such as identification of ringleaders in anti-criminal and anti-terrorist network, efficient storage of data in Wireless Sensor Network （WSN）. At present, most of community identification methods still require the speci- fications of the number or the scale of community by user and still can not handle overlapping nodes. In an at- tempt to solve these problems, a network community identification method based on utility value is proposed, which is a function of each node＇ s clustering coefficient and degree. This method makes use of individual-cen- tered theory for reference and can automatically determine the number of communities. In addition, this method is an overlapping community identification method in nature. It is shown through eontrastive experiments that this method is more efficient than other methods based on individual-centered theory when they control the same amount of information. Finally, a research direction is proposed for network community identification method based on the individual-centered theory.
同期刊论文项目
同项目期刊论文
期刊信息
位置：