Evolving networks by merging cliques
Evolving networks by merging cliques
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DOI：
10.1103/physreve.72.046116
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发表时间：
2005-10-01
影响因子：
2.4
通讯作者：
Oosawa, C
中科院分区：
文献类型：
--
作者：
Takemoto, K;Oosawa, C
我们提出了一个进化网络的模型，通过合并用完全图表示的构建块，让人想起生物系统中的模块或社会学中的社区。该模型具有幂律度分布、幂律聚类谱和与网络大小无关的高平均聚类系数。解析解表明，度指数由合并节点数与块中所有节点数之比决定，表明度指数是可调的，并且也适用于块是经典网络（如Erdos-Renyi）或正则图。我们的模型在特定条件下与Barabasi-Albert模型成为同一模型。
We propose a model for evolving networks by merging building blocks represented as complete graphs, reminiscent of modules in biological system or communities in sociology. The model shows power-law degree distributions, power-law clustering spectra, and high average clustering coefficients independent of network size. The analytical solutions indicate that a degree exponent is determined by the ratio of the number of merging nodes to that of all nodes in the blocks, demonstrating that the exponent is tunable, and are also applicable when the blocks are classical networks such as Erdos-Renyi or regular graphs. Our model becomes the same model as the Barabasi-Albert model under a specific condition.