Topological structural classes of complex networks
Topological structural classes of complex networks
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DOI：
10.1103/physreve.75.016103
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发表时间：
2007-01-01
影响因子：
2.4
通讯作者：
Estrada, Ernesto
中科院分区：
文献类型：
--
作者：
Estrada, Ernesto
我们使用理论原理来研究复杂网络如何在大规模上进行拓扑组织。使用谱图理论，我们预测存在四种不同的拓扑结构类的网络。这些类别分别对应于缺乏结构瓶颈的高度同质网络，组织成具有低社区间连通性的高度互连模块的网络，具有被稀疏外围包围的高度连接的中央核心的网络，以及显示高度连接的组（准分）和划分为不相交子集（准二分）的节点组的组合的网络。在这里，我们通过光谱标度方法表明，这些类确实存在于现实世界的生态，生物，信息，技术和社交网络。我们发现，没有三个网络增长机制-随机均匀分布，优先连接，随机度序列相同的真实的网络-是能够重现复杂网络的四个结构类。这些模型再现了两个网络类作为平均度的函数，但在再现其他两个网络类时完全失败。
We use theoretical principles to study how complex networks are topologically organized at large scale. Using spectral graph theory we predict the existence of four different topological structural classes of networks. These classes correspond, respectively, to highly homogenous networks lacking structural bottlenecks, networks organized into highly interconnected modules with low inter-community connectivity, networks with a highly connected central core surrounded by a sparser periphery, and networks displaying a combination of highly connected groups (quasicliques) and groups of nodes partitioned into disjoint subsets (quasibipartites). Here we show by means of the spectral scaling method that these classes really exist in real-world ecological, biological, informational, technological, and social networks. We show that neither of three network growth mechanisms-random with uniform distribution, preferential attachment, and random with the same degree sequence as real network-is able to reproduce the four structural classes of complex networks. These models reproduce two of the network classes as a function of the average degree but completely fail in reproducing the other two classes of networks.