一种基于社团和分层思想的无标度演化模型

针对复杂网络节点度分布服从幂律分布问题, 给出一种基于社团和分层思想的无标度演化模型. 该模型利用转轮思想和限制节点度改进了无标度模型的优先连接策略； 加入分层结构优化了无标度网络的搜索； 将局域世界模型中的局域世界思想引入到模型中, 并利用社团结构改进了局域世界模型中局域世界的不确定性问题.  理论分析证明了模型的度分布服从幂律分布, 且幂律指数可调.  模拟实验结果表明, 模型有较小的平均路径长度和较大的聚类系数,  且两层网络搜索效率优于单层网络搜索.

A Scale-Free Evolution Model Based on Community and Hierarchy

The thesis brought up a scale\|free model based on community and hierarchy in the light of the complex network node degree distribution obey power law distribution. The preferential attachment mechanism of BA model was improved via turntable technique and restrictions on node degree, and the searching in scale-free networks was optimized by bringing in hierarchical structure. The new model not only introduces the concept of local world but also solves the uncertainty problem in local\|world model via the community structure. Theoretical analysis proves that this model’s degree distribution obeys power\|law distribution and the power\|law index is adjustable. Simulation experiment shows that the new model has a smaller average path length and a larger clustering coefficient. While searching the network, two\|layer network is much more efficient than single-layer network. 