论无标度网的增长和择优

增长和择优机制是无标度网络中两种重要的演化机制，已发现比较重要的择优机制有度择优和秩次择优，比较重要的增长方式有星形图增长和完全图增长.该文首先分析了秩次择优机制对网络度指数的影响，指出可以利用秩次择优来构造度指数在较大范围内变化的模型. 接下来分析了星形图增长和完全图增长的优缺点，并提出了更符合实际情况的模体增长方式，然后结合秩次择优机制和模体增长方式提出了一个新模型——模体增长秩次择优模型，该模型除了具有较宽的度指数范围外，还在度指数大于2.5时具有独立于网络规模的群集系数.

Growth and Preferential Attachment in Scale-Free Networks

Growth and preferential attachment are important mechanisms in forming scale-free networks.There are two methods of important preferential attachment: degree preferential attachment and ranking preferential attachment,and two methods of important growing: star-like graph growth and complete graph growth.In this paper,ranking preferential attachment is analyzed to show that it can be used to construct model that have wide range of degree exponent.A new model with motif growth and ranking preferential attachment is then constructed.The model has a wide range of degree exponent,and its clustering coefficient is independent of the network size when the degree exponent is larger than 2.5.