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Degree correlations in graphs with clique clustering

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Mann_2022_Degree_correlations_in_graphs_PhysRevE_AAM.pdf (1003.Kb)
Date
30/04/2022
Author
Mann, Peter Stephen
Smith, V.A.
Mitchell, John B. O.
Dobson, Simon Andrew
Funder
EPSRC
Grant ID
EP/N007565/1
Keywords
Complex networks
Clustering
QA Mathematics
QA75 Electronic computers. Computer science
QC Physics
T-NDAS
MCC
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Abstract
Correlations among the degrees of nodes in random graphs often occur when clustering is present. In this paper we define a joint-degree correlation function for nodes in the giant component of clustered configuration model networks which are comprised of higher-order subgraphs. We use this model to investigate, in detail, the organisation among nearest-neighbour subgraphs for random graphs as a function of subgraph topology as well as clustering. We find an expression for the average joint degree of a neighbour in the giant component at the critical point for these networks. Finally, we introduce a novel edge-disjoint clique decomposition algorithm and investigate the correlations between the subgraphs of empirical networks.
Citation
Mann , P S , Smith , V A , Mitchell , J B O & Dobson , S A 2022 , ' Degree correlations in graphs with clique clustering ' , Physical Review. E, Statistical, nonlinear, and soft matter physics , vol. 105 , no. 4 , 044314 . https://doi.org/10.1103/PhysRevE.105.044314
Publication
Physical Review. E, Statistical, nonlinear, and soft matter physics
Status
Peer reviewed
DOI
https://doi.org/10.1103/PhysRevE.105.044314
ISSN
1539-3755
Type
Journal article
Rights
Copyright © 2022 American Physical Society. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1103/PhysRevE.105.044314.
Description
Funding: This work was partially supported by the UK Engineering and Physical Sciences Research Council under grant number EP/N007565/1 (Science of Sensor Systems Software).
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/25122

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