Bauer Roman, Kaiser Marcus
Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; Interdisciplinary Computing and Complex BioSystems Research Group (ICOS), School of Computing Science, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
R Soc Open Sci. 2017 Mar 22;4(3):160691. doi: 10.1098/rsos.160691. eCollection 2017 Mar.
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.
许多现实世界的网络都包含被称为枢纽的高度连接节点。枢纽对于网络功能和传播动态往往至关重要。然而，关于枢纽在网络发展过程中如何产生的经典模型不切实际地假设新节点能够获取有关现有节点连接性（例如度）的信息。在此，我们通过非线性增长引入枢纽形成，即每个阶段生成的节点数量随时间增加，并且新节点独立于目标节点特征形成连接。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等各类网络在连接数量、枢纽出现时间和富俱乐部组织方面的变化。此外，与先前的偏好依附或复制 - 分歧模型相比，非线性增长为这些网络提供了更通用的表示。总体而言，通过非线性网络扩展创建枢纽可作为研究许多现实世界网络发展的基准模型。
