摘要
以CAIDA组织提供的海量的Internet AS级拓扑数据作为样本数据,分析了样本的覆盖问题和采样偏见问题,并对数据进行了修正.在此基础上,对2001年到2007年之间的Internet AS级拓扑数据进行了社团划分,并计算了模块度、社团规模等与社团演化相关的特征量,发现Internet AS级拓扑数据的社团特征越来越明显;同时,根据社团相关特征量分析了AS域内节点的动态行为特征.最后,根据分析结果研究了导致社团特征演化的成因.
Taking the mass topological data at the Internet AS (autonomous system) level provided by CAIDA as sample data, the sample coverage and prejudice in sampling are discussed to correct the sample data. Then, the topological data at the Internet AS level from 2001 to 2007 are divided into different communities, and the eigenvalues relevant to community evolution are calculated, such as the modularity and community size. The results showed that the topological data at the Internet AS level exhibit more and more apparent characteristics of community gradually. Simultaneously, the dynamical behavior of the nodes in the AS domain is analyzed according to the eigenvalues relevant to community evolution, and what causes the evolution is analyzed as well.
出处
《东北大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2010年第2期181-184,共4页
Journal of Northeastern University(Natural Science)
基金
高等学校科技创新工程重大项目培育资金资助项目(708026)