Research article Special Issues

On knot separability of hypergraphs and its application towards infectious disease management

  • Received: 07 October 2022 Revised: 03 February 2023 Accepted: 16 February 2023 Published: 23 February 2023
  • MSC : 05C05, 05C38, 05C65, 05C90

  • The article deals with some theoretical aspects of hypergraph connectivity from the knot view. The strength of knots is defined and investigates some of their properties. We introduce the concept of cut knot and investigate its importance in the connectivity of hypergraphs. We also introduce the concept of hypercycle in terms of knot hyperpath and establish a sufficient condition for a hypergraph to be a hypertree in terms of the strength of knots. Cyclic hypergraph is defined in terms of a permutation on the set of hyperedges and could be an interesting topic for investigation in the sense that it can be linked with the notion of a permutation group. An algorithm is modelled to construct a tree and hypertree from the strength of knots of a hypertree. Lastly, a model of a hypergraph is constructed to control the spread of infection for an infectious disease with the help of the strength of knots.

    Citation: Raju Doley, Saifur Rahman, Gayatri Das. On knot separability of hypergraphs and its application towards infectious disease management[J]. AIMS Mathematics, 2023, 8(4): 9982-10000. doi: 10.3934/math.2023505

    Related Papers:

  • The article deals with some theoretical aspects of hypergraph connectivity from the knot view. The strength of knots is defined and investigates some of their properties. We introduce the concept of cut knot and investigate its importance in the connectivity of hypergraphs. We also introduce the concept of hypercycle in terms of knot hyperpath and establish a sufficient condition for a hypergraph to be a hypertree in terms of the strength of knots. Cyclic hypergraph is defined in terms of a permutation on the set of hyperedges and could be an interesting topic for investigation in the sense that it can be linked with the notion of a permutation group. An algorithm is modelled to construct a tree and hypertree from the strength of knots of a hypertree. Lastly, a model of a hypergraph is constructed to control the spread of infection for an infectious disease with the help of the strength of knots.



    加载中


    [1] M. J. Keeling, K. T. Eames, Networks and epidemic models, J. R. Soc. Interface, 2 (2005), 295–307. https://doi.org/10.1098/rsif.2005.0051
    [2] M. F. Capobianco, J. C. Molluzzo, The strength of a graph and its application to organizational structure, Soc. Networks, 2 (1979), 275–283. https://doi.org/10.1016/0378-8733(79)90018-2 doi: 10.1016/0378-8733(79)90018-2
    [3] S. Rahman, M. Chowdhury, A. Firos, I. Cristea, Knots and knot-hyperpaths in hypergraphs, Mathematics, 10 (2022). https://doi.org/10.3390/math10030424
    [4] M. Dewar, D. Pike, J. Proos, Connectivity in hypergraphs, Can. Math. Bull., 61 (2018), 252–271. https://doi.org/10.4153/CMB-2018-005-9
    [5] R. Aliguliyev, R. Aliguliyev, F. Yusifov, Graph modelling for tracking the COVID-19 pandemic spread, Infect. Dis. Model., 6 (2020), 112–122. https://doi.org/10.1016/j.idm.2020.12.002 doi: 10.1016/j.idm.2020.12.002
    [6] H. R. Bhapkar, P. Mahalle, P. S. Dhotre, Virus graph and COVID-19 pandemic: A graph theory approach, In: Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach, Cham: Springer, 2020. https://doi.org/10.1007/978-3-030-55258-9_2
    [7] M. A. Khan, A. Atangana, Modeling the dynamics of novel coronavirus (2019-nCov) with fractional derivative, Alex. Eng. J., 59 (2020), 2379–2389. https://doi.org/10.1016/j.aej.2020.02.033 doi: 10.1016/j.aej.2020.02.033
    [8] G. F. Arruda, G. Petri, Y. Moreno, Social contagion models on hypergraphs, Phys. Rev. Res., 2 (2020). https://doi.org/10.1103/PhysRevResearch.2.023032
    [9] D. J. Higham, H. L. de Kergorlay, Epidemics on hypergraphs: Spectral thresholds for extinction, Proc. Math. Phys. Eng. Sci., 477 (2021). https://doi.org/10.1098/rspa.2021.0232
    [10] C. Berge, Graphs and Hypergraphs, Amsterdam: North Holland, 1973.
    [11] A. Bretto, Hypergraph Theory: An Introduction, Cham: Springer, 2013.
    [12] V. I. Voloshin, Introduction to Graph and Hypergraph, New York: Nova Science Publishers, 2013.
    [13] D. B. West, Introduction to Graph Theory, Prentice Hall, 1996.
    [14] R. Dharmarajan, K. Kannan, Hyper paths and hyper cycles, Int. J. Pure Appl. Math., 98 (2015), 309–312. https://doi.org/10.12732/ijpam.v98i3.2
    [15] P. Jégoua, S. N. Ndiaye, On the notion of cycles in hypergraphs, Descrete Math., 309 (2009), 6535–6543. https://doi.org/10.1016/j.disc.2009.06.035 doi: 10.1016/j.disc.2009.06.035
    [16] J. Wang, T. T. Lee, Paths and cycles of hypergraphs, Sci. China Ser. A-Math., 42 (1999), 1–12. https://doi.org/10.1007/BF02872044 doi: 10.1007/BF02872044
    [17] M. A. Bahmanian, M. Šajna, Connection and separation in hypergraph, Theory Appl. Graphs, 2 (2015). https://doi.org/10.20429/tag.2015.020205
    [18] A. Brandstädt, F. Dragan, V. Chepoi, V. Voloshin, Dually chordal graphs, SIAM J. Discrete Math., 11 (1998), 437–455. https://doi.org/10.1137/S0895480193253415
    [19] C. S. M. Currie, J. W. Fowler, K. Kotiadis, T. Monks, B. S. Onggo, D. A. Robertson, et al., How simulation modelling can help reduce the impact of COVID-19, J. Simul., 14 (2020), 83–97. https://doi.org/10.1080/17477778.2020.1751570 doi: 10.1080/17477778.2020.1751570
    [20] M. R. Davahli, W. Karwowski, K. Fiok, A. Murata, N. Sapkota, F. V. Farahani, et al., The COVID-19 infection diffusion in the US and Japan: A graph-theoretical approach, Biology, 11 (2022), 125. https://doi.org/10.3390/biology11010125 doi: 10.3390/biology11010125
    [21] B. Ivorra, M. R. Ferrändez, M. Vela-Pérez, A. M. Ramos, Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) considering its particular characteristics. The case of China, Commun. Nonlinear Sci. Numer. Simul., 88 (2020). https://doi.org/10.1016/j.cnsns.2020.105303
    [22] A. J. Kucharski, T. W. Russell, C. Diamond, Y. Liu, J. Edmunds, S. Funk, et al., Early dynamics of transmission and control of COVID-19: A mathematical modelling study, Lancet Infect. Dis., 20 (2020), 553–558. https://doi.org/10.1016/S1473-3099(20)30144-4 doi: 10.1016/S1473-3099(20)30144-4
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1235) PDF downloads(61) Cited by(0)

Article outline

Figures and Tables

Figures(9)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog