Coprime networks of the composite numbers: Pseudo-randomness and synchronizability

Md Rahil Miraj, Dibakar Ghosh, Chittaranjan Hens

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this paper, we propose a network whose nodes are labeled by the composite numbers and two nodes are connected by an undirected link if they are relatively prime to each other. As the size of the network increases, the network will be connected whenever the largest possible node index n≥49. To investigate how the nodes are connected, we analytically describe that the link density saturates to 6/π2, whereas the average degree increases linearly with slope 6/π2 with the size of the network. To investigate how the neighbors of the nodes are connected to each other, we find the shortest path length will be at most 3 for 49≤n≤288 and it is at most 2 for n≥289. We also derive an analytic expression for the local clustering coefficients of the nodes, which quantifies how close the neighbors of a node to form a triangle. We also provide an expression for the number of r-length labeled cycles, which indicates the existence of a cycle of length at most O(logn). Finally, we show that this graph sequence is actually a sequence of weakly pseudo-random graphs. We numerically verify our observed analytical results. As a possible application, we have observed less synchronizability (the ratio of the largest and smallest positive eigenvalue of the Laplacian matrix is high) as compared to Erdős–Rényi random network and Barabási–Albert network. This unusual observation is consistent with the prolonged transient behaviors of ecological and predator–prey networks which can easily avoid the global synchronization.

Original languageEnglish
Pages (from-to)96-110
Number of pages15
JournalDiscrete Applied Mathematics
Volume355
DOIs
StatePublished - 15 Oct 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 Elsevier B.V.

Keywords

  • Network science
  • Prime and composite numbers
  • Pseudo-random graph

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