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.
Original languageEnglish
Article number044314
Number of pages14
JournalPhysical Review. E, Statistical, nonlinear, and soft matter physics
Issue number4
Early online date20 Apr 2022
Publication statusPublished - 30 Apr 2022


  • Complex networks
  • Clustering


Dive into the research topics of 'Degree correlations in graphs with clique clustering'. Together they form a unique fingerprint.

Cite this