2021
DOI: 10.1038/s41598-021-94105-8
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Sublinear domination and core–periphery networks

Abstract: In this paper we devise a generative random network model with core–periphery properties whose core nodes act as sublinear dominators, that is, if the network has n nodes, the core has size o(n) and dominates the entire network. We show that instances generated by this model exhibit power law degree distributions, and incorporates small-world phenomena. We also fit our model in a variety of real-world networks.

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Cited by 4 publications
(11 citation statements)
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“…The latter are closely associated with node centrality measures [20], and, of course, a continuous score can be used for subsequent ranking and partitioning. Second, while there is general consistency around the principle that core nodes should be well-connected and peripheral nodes should be poorly connected, there is a choice to be made about whether edges that join a core node and a peripheral node should occur with high/intermediate frequency [11,27,30,32] or low frequency [15], or whether such edges are irrelevant [5,14,20].…”
Section: Related Workmentioning
confidence: 99%
“…The latter are closely associated with node centrality measures [20], and, of course, a continuous score can be used for subsequent ranking and partitioning. Second, while there is general consistency around the principle that core nodes should be well-connected and peripheral nodes should be poorly connected, there is a choice to be made about whether edges that join a core node and a peripheral node should occur with high/intermediate frequency [11,27,30,32] or low frequency [15], or whether such edges are irrelevant [5,14,20].…”
Section: Related Workmentioning
confidence: 99%
“…Core-periphery networks have been widely observed in the modern financial (inter-bank) system [CWY16, EGJ14, FL15, CVP14, IVvdLH20] whereas the core consists of a few large banks (see e.g. [Pap21,FL15] for a characterization of the core size of core-periphery networks) and more small banks. We generate a network of n = 50 nodes and T = 10 rounds whereas the structural graphs G t are drawn i.i.d.…”
Section: Stochastic Blockmodel With Core-periphery Structurementioning
confidence: 99%
“…In this structure, the network is roughly comprised of a core and a periphery. The core is a set of nodes where the nodes are tightly connected with one another, and almost cover the periphery of the network [31]. The nodes that lie within the periphery of the graph are sparsely connected with one another and connected to the core.…”
Section: Introductionmentioning
confidence: 99%
“…The nodes that lie within the periphery of the graph are sparsely connected with one another and connected to the core. In terms of generative model formulations [15,31,47] study core-periphery structure on graphs and unravel interesting insights regarding the core of the network and its identification. Remarkably, the recent work of [31] observes that the core of (small-scale) real-world graphs is of sublinear size wrt.…”
Section: Introductionmentioning
confidence: 99%
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