2019
DOI: 10.1007/s42486-019-00016-z
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Recent advance on detecting core-periphery structure: a survey

Abstract: Recently, one type of mesoscale structure called core-periphery (CP) structure has received much attention in complex networks, as the algorithmic detection of such structures makes it possible to discover network features that are not apparent either at the local scale of nodes and edges or at the global scale of summary statistics. The core-periphery structure refers to that core nodes are densely interconnected, while periphery nodes are connected to core nodes to different extents, and periphery nodes are … Show more

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Cited by 12 publications
(7 citation statements)
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References 81 publications
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“…Indeed, in real-world networks, the density is inhomogeneous. Te community [15][16][17] and core-periphery [18,19] structures are good features to capture this phenomenon. Indeed, communities are dense clusters of nodes loosely connected.…”
Section: Component Structurementioning
confidence: 99%
“…Indeed, in real-world networks, the density is inhomogeneous. Te community [15][16][17] and core-periphery [18,19] structures are good features to capture this phenomenon. Indeed, communities are dense clusters of nodes loosely connected.…”
Section: Component Structurementioning
confidence: 99%
“…This density notion is the translation of usual graph density into the world of link clustering (Havemann et al, 2019, p. 5). For a recent review of algorithms for core-periphery construction see the paper by Tang, Zhao, et al (2019).…”
Section: Cores and Peripheries Of Link Clusters: The Cplc Algorithmmentioning
confidence: 99%
“…Csermely et al [28], in their extensive paper, describe in detail the concepts and definitions of basic core-periphery structures and their functions in different types of networks. The article of Tang et al [31] focuses on the description of methods for the detection of core-periphery structures in networks. Alternative detection methods also can be found [32,33].…”
Section: Related Workmentioning
confidence: 99%