ArticleScientific reports2017
Geometric explanation of the rich-club phenomenon in complex networks.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Abnormal Alterations of the White Matter Structural Network in Patients with Herpes Zoster and Postherpetic Neuralgia.Brain topography · 2025Article
- Analysis of Weather Factors on Aircraft Cancellation using a Multilayer Complex Network.Entropy (Basel, Switzerland) · 2023Article
- Altered brain networks and connections in chronic heart failure patients complicated with cognitive impairment.Frontiers in aging neuroscience · 2023Article
- The Local Topological Reconfiguration in the Brain Network After Targeted Hub Dysfunction Attacks in Patients With Juvenile Myoclonic Epilepsy.Frontiers in neuroscience · 2022Article
- The 2-D Cluster Variation Method: Topography Illustrations and Their Enthalpy Parameter Correlations.Entropy (Basel, Switzerland) · 2021Article
- Estimating degree-degree correlation and network cores from the connectivity of high-degree nodes in complex networks.Scientific reports · 2020Article
- Resilience or robustness: identifying topological vulnerabilities in rail networks.Royal Society open science · 2019Article
- Rich-club neurocircuitry: function, evolution, and vulnerability.Dialogues in clinical neuroscience · 2018Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rich club organization (the presence of highly connected hub core in a network) influences many structural and functional characteristics of networks including topology, the efficiency of paths and distribution of load. Despite its major role, the literature contains only a very limited set of models capable of generating networks with realistic rich club structure. One possible reason is that the rich club organization is a divisive property among complex networks which exhibit great diversity, in contrast to other metrics (e.g. diameter, clustering or degree distribution) which seem to behave very similarly across many networks. Here we propose a simple yet powerful geometry-based growing model which can generate realistic complex networks with high rich club diversity by controlling a single geometric parameter. The growing model is validated against the Internet, protein-protein interaction, airport and power grid 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.