the Graph point – symmetric

Cayley graph has been introduced by A . Cayley , which is point – symmetric . However in this paper , I have found another type of symmetric graph , which is not Cayley graph . This graph is called peterson graph with 10 vertices is proposed

Robustness of network coherence in asymmetric unicyclic graphs

In this paper, we propose a family of unicyclic graphs to study robustness of network coherence quantified by the Laplacian spectrum, which measures the extent of consensus under the noise. We adjust the network parameters to change the structural asymmetries with an aim of studying their effects on the coherence. Using the graph’s structures and matrix theories, we obtain closed-form solutions of the network coherence regarding network parameters and network size. We further show that the coherence of the asymmetric graph is higher than the corresponding symmetric graph and also compare the consensus behaviors for the graphs with different asymmetric structures. It displays that the coherence of the unicyclic graph with one hub is better than the graph with two hubs. Finally, we investigate the effect of degree of hub nodes on the coherence and find that bigger difference of degrees leads to better coherence.

Heptavalent Symmetric Graphs with Certain Conditions

A graph [Formula: see text] is said to be symmetric if its automorphism group [Formula: see text] acts transitively on the arc set of [Formula: see text]. We show that if [Formula: see text] is a finite connected heptavalent symmetric graph with solvable stabilizer admitting a vertex-transitive non-abelian simple group [Formula: see text] of automorphisms, then either [Formula: see text] is normal in [Formula: see text], or [Formula: see text] contains a non-abelian simple normal subgroup [Formula: see text] such that [Formula: see text] and [Formula: see text] is explicitly given as one of 11 possible exceptional pairs of non-abelian simple groups. If [Formula: see text] is arc-transitive, then [Formula: see text] is always normal in [Formula: see text], and if [Formula: see text] is regular on the vertices of [Formula: see text], then the number of possible exceptional pairs [Formula: see text] is reduced to 5.

FUNGSI SIMETRI TERHADAP GARIS x = a DAN SIFAT-SIFATNYA

An even function is a function with a graph that is symmetric with respect to the y-axis or the line x = 0. In this paper, we will introduce a more general function of the even function, we call it as symmetry function with respect to the line x = a, which is a function whose graph is symmetric with respect to the line x = a. This paper discusses the properties of the symmetry function with respect to the line x = a, which is derived from the pre-existing properties of the even function. Some of the results obtained above, the linear combination of the symmetry functions with respect to the line x = a is a symmetry function with respect to the line x = a and the composition of any function with a symmety function with respect to the line x = a is a symmetry function with respect to the line x = a. Keywords: Even function, a symmetry function with respect to the line x=a, symmetric graph

Simulation of bimolecular reactions: Numerical challenges with the graph Laplacian

An important framework for modelling and simulation of chemical reactions is a Markov process sometimes known as a master equation. Explicit solutions of master equations are rare; in general the explicit solution of the governing master equation for a bimolecular reaction remains an open question. We show that a solution is possible in special cases. One method of solution is diagonalization. The crucial class of matrices that describe this family of models are non-symmetric graph Laplacians. We illustrate how standard numerical algorithms for finding eigenvalues fail for the non-symmetric graph Laplacians that arise in master equations for models of chemical kinetics. We propose a novel way to explore the pseudospectra of the non-symmetric graph Laplacians that arise in this class of applications, and illustrate our proposal by Monte Carlo. Finally, we apply the Magnus expansion, which provides a method of simulation when rates change in time. Again the graph Laplacian structure presents some unique issues: standard numerical methods of more than second-order fail to preserve positivity. We therefore propose a method that achieves fourth-order accuracy, and maintain positivity. References A. Basak, E. Paquette, and O. Zeitouni. Regularization of non-normal matrices by gaussian noise–-the banded toeplitz and twisted toeplitz cases. In Forum of Mathematics, Sigma, volume 7. Cambridge University Press, 2019. doi:10.1017/fms.2018.29. S. Blanes, F. Casas, J. A. Oteo, and J. Ros. The magnus expansion and some of its applications. Phys. Rep., 470(5-6):151–238, 2009. doi:10.1016/j.physrep.2008.11.001. B. A. Earnshaw and J. P. Keener. Invariant manifolds of binomial-like nonautonomous master equations. SIAM J. Appl. Dyn. Sys., 9(2):568–588, 2010. doi10.1137/090759689. J. Gunawardena. A linear framework for time-scale separation in nonlinear biochemical systems. PloS One, 7(5):e36321, 2012. doi:10.1371/journal.pone.0036321. A. Iserles and S. MacNamara. Applications of magnus expansions and pseudospectra to markov processes. Euro. J. Appl. Math., 30(2):400–425, 2019. doi:10.1017/S0956792518000177. S. MacNamara. Cauchy integrals for computational solutions of master equations. ANZIAM Journal, 56:32–51, 2015. doi:10.21914/anziamj.v56i0.9345. S. MacNamara, A. M. Bersani, K. Burrage, and R. B. Sidje. Stochastic chemical kinetics and the total quasi-steady-state assumption: Application to the stochastic simulation algorithm and chemical master equation. J. Chem. Phys., 129:095105, 2008. doi:10.1063/1.2971036. S. MacNamara and K. Burrage. Stochastic modeling of naive T cell homeostasis for competing clonotypes via the master equation. SIAM Multiscale Model. Sim., 8(4):1325–1347, 2010. S. MacNamara, K. Burrage, and R. B. Sidje. Multiscale modeling of chemical kinetics via the master equation. SIAM Multiscale Model. and Sim., 6(4):1146–1168, 2008. doi:10.1137/060678154. S. MacNamara, Wi. McLean, and K. Burrage. Wider contours and adaptive contours, pages 79–98. Springer International Publishing, 2019. doi:10.1007/978-3-030-04161-8_7. M. J. Shon. Trapping and manipulating single molecules of DNA. PhD thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:11744428. M. J. Shon and A. E. Cohen. Mass action at the single-molecule level. J. Am. Chem. Soc., 134(35):14618–14623, 2012. doi:10.1021/ja3062425. C. Timm. Random transition-rate matrices for the master equation. Phys. Rev. E, 80(2):021140, 2009. doi:10.1103/PhysRevE.80.021140. L. N. Trefethen and M. Embree. Spectra and pseudospectra: The behavior of nonnormal matrices and operators. Princeton University Press, 2005. https://press.princeton.edu/books/hardcover/9780691119465/spectra-and-pseudospectra.