Prominence and control: the weighted rich-club effect.

By Tore Opsahl, Vittoria Colizza, Pietro Panzarasa, José J. Ramasco. Physical Review Letters, 2008

378 citations recorded by the source corpus.

Complex systems are often characterized by large-scale hierarchical organizations. Whether the prominent elements, at the top of the hierarchy, share and control resources or avoid one another lies at the heart of a system's global organization and functioning. Inspired by network perspectives, we propose a new general framework for studying the tendency of prominent elements to form clubs with exclusive control over the majority of a system's resources. We explore associations between prominence and control in the fields of transportation, scientific collaboration, and online communication.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2c85f90c6f6b15474bc5b6ef46d312797dc29045

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In Management Science and Operations Research

In Statistical and Nonlinear Physics

Citations in the corpus

Task-Based Core-Periphery Organization of Human Brain Dynamics (2012, background): “) The RCC has also been extended to weighted networks [38], but it still requires one to specify a threshold value of richness to enable one to ask whether ‘‘rich’’ nodes tend to connect to other ‘‘rich’’ nodes.”
Water for food: The global virtual water trade network (2011, background): “This phenomenon occurs when prominent elements in a system engage in stronger or weaker interactions among themselves than expected by pure chance [Opsahl et al., 2008].”
High-cost, high-capacity backbone for global brain communication (2012, background): “Formally, the rich club coefficient Φ(k) is given by (13)”
Network resilience (2020, background): “Rich club phenomenon examines the group tendency of prominent elements at the top of the network hierarchy to control the majority of a system’s resources [604].”
Clustering in weighted networks (2009, background): “The sixth dataset is the network of the 500 busiest commercial airports in the United States (Colizza et al., 2007; Opsahl et al., 2008 )9.”
Rich-Club Organization of the Human Connectome (2011, methodology): “Following a similar principle, for weighted networks, a weighted rich-club parameter w(k) was computed (Opsahl et al., 2008).”
Abnormal rich club organization and functional brain dynamics in schizophrenia. (2013, neutral): “9) [37-88] 57.”
Post-Mortem Examination of the International Financial Network (2012, neutral): “To explore this issue, we have computed the rich-club coefficient (RCC ) as in Opsahl et al. (2008).”
Multi-scale brain networks (2016, neutral): “We recall that a rich club is a group of hubs (high degree, high strength nodes) that are also densely interconnected to one another (Colizza et al., 2006; Opsahl et al., 2008).”
Node centrality in weighted networks: Generalizing degree and shortest paths (2010, methodology): “In fact, it has been shown that the nodes with the highest node strength are likely to be strongly connected in networks from a range of different domains (Opsahl et al., 2008).”
Patterns and dynamics of users' behavior and interaction: Network analysis of an online community (2009, result): “…DOI: 10.1002/asi for each value of out-degree (in-degree), we created subsets of users that have an out-degree (in-degree) of at least that value (Opsahl et al., 2008), and measured the proportion of men in these subsets compared to the overall proportion in the whole system (insets in Figure 3a…”
Triadic closure in two-mode networks: Redefining the global and local clustering coefficients (2010, methodology): “This has direct implications for frameworks that utilize random networks to detect a baseline level (e.g., Opsahl et al., 2008) and when comparing measures observed in a network with those found in corresponding random networks.”