中文摘要：

传统BA无标度网络模型中节点的最大度随着网络规模无限增大。使得网络中存在少数度非常大的hub节点。考虑实际的网络构建过程中由于成本限制，节点的最大度都是有限的，因此本文在BA无标度网络模型生长规则的基础上提出最大度受限的BA网络模型一LBA网络模型。并进一步研究最大度限制K不同取值下，LBA网络的平均路径、聚类系数和度分布情况，并和近邻网络、随机网络和BA网络的统计特性进行比较。研究表明，K接近网络平均度时，生成的网络类似近邻网络，当K接近2倍平均度时，LBA网络的统计特性接近随机网络，当K较大时。LBA网络的统计特性接近原始BA无标度网络，因此通过控制最大度约束K的取值，网络可以实现从近邻网络模型到随机网络模型到无标度网络模型的过渡。

英文摘要：

In traditional BA scale-free networks,the maximum degree of nodes increases unlimitedly with the scale of networks ,which causes some hub nodes with large degree existing in the networks. Considering the cost of constructing the real networks ,the maximum degree of nodes is limited. According to the growing rules of the BA network,this paper proposes a limited-maximum-degree BA network model, called LBA network,then further studies the average path length,clustering coefficient and degree distribution of LBA network under different values of the restriction of maximum degree K,and compares them with the statistical properties of the neighbor network,the random network and the BA network. Research shows that the generated LBA network is similar to the neighbor network when K is close to the network average degree,LBA network properties are close to the random network when K is about 2 times of the network average degree,and the statistical properties of LBA network are close to the original BA scale-free network with a relative larger K.