ArticleRoyal Society open science2017
Nonlinear growth: an origin of hub organization in complex networks.
Article in Royal Society open science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Metric structural human connectomes: Localization and multifractality of eigenmodes.Network neuroscience (Cambridge, Mass.) · 2025Article
- Creative Destruction: A Basic Computational Model of Cortical Layer Formation.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Adaptive Rewiring in Weighted Networks Shows Specificity, Robustness, and Flexibility.Frontiers in systems neuroscience · 2021Article
- Spatiotemporal ontogeny of brain wiring.Science advances · 2019Article
- The development of brain network hubs.Developmental cognitive neuroscience · 2019Review
- A blueprint of mammalian cortical connectomes.PLoS biology · 2019Article
- Topological reinforcement as a principle of modularity emergence in brain networks.Network neuroscience (Cambridge, Mass.) · 2019Article
- Rich-club neurocircuitry: function, evolution, and vulnerability.Dialogues in clinical neuroscience · 2018Review
- The impact of epilepsy surgery on the structural connectome and its relation to outcome.NeuroImage. Clinical · 2018Article
- Generative models for network neuroscience: prospects and promise.Journal of the Royal Society, Interface · 2017Review
- Nonlinear growth: an origin of hub organization in complex networks.Royal Society open science · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.