Nonlinear growth: an origin of hub organization in complex networks.
Nonlinear growth: an origin of hub organization in complex networks.
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DOI：
10.1098/rsos.160691
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发表时间：
2017-03
影响因子：
3.5
通讯作者：
Kaiser M
中科院分区：
文献类型：
--
作者：
Bauer R;Kaiser M
许多现实世界的网络都包含称为集线器的高度连接的节点。集线器通常对网络功能和传播动态至关重要。然而，在网络开发过程中，集线器如何起源的经典模型不切实际地假设新节点获得有关现有节点的连接性(例如程度)的信息。在这里，我们通过非线性增长引入枢纽形成，其中每个阶段生成的节点数量随着时间的推移而增加，新节点形成独立于目标节点特征的连接。我们的模型再现了从蛋白质-蛋白质、神经元和纤维束脑网络到航空网络的连接数量、中枢发生时间和丰富的俱乐部组织网络的变化。此外，与以前的优先依附或复制-发散模型相比，非线性增长给出了这些网络更一般的表示。总体而言，通过非线性网络扩展创建的中心可以作为研究许多现实世界网络发展的基准模型。
Many real-world networks contain highly connected nodes called hubs. Hubs are often crucial for network function and spreading dynamics. However, classical models of how hubs originate during network development unrealistically assume that new nodes attain information about the connectivity (for example the degree) of existing nodes. Here, we introduce hub formation through nonlinear growth where the number of nodes generated at each stage increases over time and new nodes form connections independent of target node features. Our model reproduces variation in number of connections, hub occurrence time, and rich-club organization of networks ranging from protein–protein, neuronal and fibre tract brain networks to airline networks. Moreover, nonlinear growth gives a more generic representation of these networks compared with previous preferential attachment or duplication–divergence models. Overall, hub creation through nonlinear network expansion can serve as a benchmark model for studying the development of many real-world networks.