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On the dominant local metric dimension of certain polyphenyl chain graphs

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Abstract

The most practical application of graph theory in chemistry is to represent molecules as graphs, where the vertices represent atoms and the edges represent bonds between the atoms. Let G be a molecular graph with vertex set V(G) and edge set E(G). An atom (vertex) v resolves pair of adjacent vertices \(v_1\) and \(v_2\), if \(d(v,v_1)\ne d(v,v_2)\), where \(d(v,v_i)\) is the lenght of the shortest path joining v and \(v_i\). An ordered set \(W_l\subseteq V(G)\) is called a local resolving set, if \(W_l\) resolves each pair of adjacent vertices of G. A subset \(W\subseteq V(G)\) is said to be dominating set, if for each vertex \(u\in V(G){\setminus } W\), there is a vertex \(v\in W\), such that u is adjacent to v in G. An ordered set \(W_l=\{w_1,w_2,w_3,...,w_k\}\subseteq V(G)\) is called dominant local resolving set, if \(W_l\) is dominating set and also a local resolving set, and its minimum cardinality is known as dominant local metric dimension. In this article, the dominant local metric dimension of ortho-polyphenyl chain graph \(O_n\), meta-polyphenyl chain graph \(M_n\), para-polyphenyl chain graph \(L_n\), and the linear [n]-tetracene chain graph T[n] are determined. Furthermore, the bond number, mobile bond order, free valence index, and total \(\pi\)-electron energy of these polyphenyl chain graphs are studied. These findings are helpful for the structure-activity relationship analysis of polyphenyl structures.

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References

  1. Flower DR (1998) On the properties of bit string-based measures of chemical similarity. J Chem Inf Comput Sci 38(3):379–386

    Article  CAS  Google Scholar 

  2. Li QR, Yang Q, Yin H, Yang S (2004) Analysis of by-products from improved ullmann reaction using TOFMS and GCTOFMS. J Univ Sci Technol China 34:335–341

    CAS  Google Scholar 

  3. Tepavcevic S, Wroble AT, Bissen M, Wallace DJ, Choi Y, Hanley L (2005) Photoemission studies of polythiophene and polyphenyl films produced via surface polymerization by ion-assisted deposition. J Phys Chem B 109(15):7134–7140. https://doi.org/10.1021/jp0451445

    Article  CAS  PubMed  Google Scholar 

  4. Johnson M (1993) Structure-activity maps for visualizing the graph variables arising in drug design. J Biopharm Stat 3(2):203–236. https://doi.org/10.1080/10543409308835060

    Article  CAS  PubMed  Google Scholar 

  5. Zhang Z, Cui P, Zhu W (2020) Deep learning on graphs: a survey. IEEE Trans Knowl Data Eng 34:249–270

    Article  Google Scholar 

  6. Samanta B, De A, Jana G, Gómez V, Chattaraj PK, Ganguly N, Gomez-Rodriguez M (2020) Nevae: A deep generative model for molecular graphs. J Mach Learn Res 21(114):1–33

    Google Scholar 

  7. Xu Y, Lin K, Wang S, Wang L, Cai C, Song C, Lai L, Pei J (2019) Deep learning for molecular generation. Future Med Chem 11(6):567–97

    Article  CAS  PubMed  Google Scholar 

  8. Kell DB, Samanta S, Swainston N (2020) Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently. Biochem J 477(23):4559–4580

    Article  CAS  PubMed  Google Scholar 

  9. Slater PJ (1975) Leaves of trees. Congr Numer 14(37):549–559

    Google Scholar 

  10. Harary F, Melter RA (1976) On the metric dimension of a graph. Ars Combin 2:191–195

    Google Scholar 

  11. Chartrand G, Eroh L, Johnson MA, Oellermann OR (2000) Resolvability in graphs and the metric dimension of a graph. Discret Appl Math 105:99–113

    Article  Google Scholar 

  12. Chartrand G, Zhang P (2003) The theory and applications of resolvability in graphs. Congr Numer 160:47–68

    Google Scholar 

  13. Chartrand G, Poisson C, Zhang P (2000) Resolvability and the upper dimension of graphs. Comput Math with Appl 39:19–28. https://doi.org/10.1016/S0898-1221(00)00126-7

    Article  Google Scholar 

  14. Khuller S, Raghavachari B, Rosenfeld A (1996) Landmarks in graphs. Discret Appl Math 70(3):217–229. https://doi.org/10.1016/0166-218X(95)00106-2

    Article  Google Scholar 

  15. Cáceres J, Hernando C, Mora M, Pelayo IM, Puertas ML, Seara C, Wood DR (2007) On the metric dimension of cartesian products of graphs. SIAM J Discrete Math 21(2):423–441. https://doi.org/10.1137/050641867

    Article  Google Scholar 

  16. Melter RA, Tomescu I (1984) Metric bases in digital geometry. Comput vis graph image Process 25(1):113–121. https://doi.org/10.1016/0734-189X(84)90051-3

    Article  Google Scholar 

  17. Imran M, Baig AQ, Ahmad A (2012) Families of plane graphs with constant metric dimension. Util math 88:43–57

    Google Scholar 

  18. Okamoto F, Phinezy B, Zhang P (2010) The local metric dimension of a graph. Math Bohem 135(3):239–255

    Article  Google Scholar 

  19. Rodríguez-Velázquez JA, Barragán-Ramírez GA, García Gómez C (2016) On the local metric dimension of corona product graphs. Bull Malays Math Sci Soc 39(1):157–173

    Article  Google Scholar 

  20. Rodríguez-Velázquez JA, García Gómez C, Barragán-Ramírez GA (2015) Computing the local metric dimension of a graph from the local metric dimension of primary subgraphs. Int J Comput Math 92(4):686–693. https://doi.org/10.1080/00207160.2014.918608

    Article  Google Scholar 

  21. Barragán-Ramírez GA, Rodríguez-Velázquez JA (2016) The local metric dimension of strong product graphs. Graphs Combin 32:1263–1278. https://doi.org/10.1007/s00373-015-1653-z

    Article  Google Scholar 

  22. Haynes TW, Hedetniemi S, Slater P (2013) Fundamentals of domination in graphs. Florida, Boca Raton

    Book  Google Scholar 

  23. Chartrand G, Lesniak L, Zhang P (1996) Graphs & digraphs. Chapman & Hall, London

    Google Scholar 

  24. Brigham RC, Chartrand G, Dutton RD, Zhang P (2003) Resolving domination in graphs. Math Bohem 128(1):25–36. https://doi.org/10.21136/MB.2003.133935

    Article  Google Scholar 

  25. Susilowati L, Sa’adah I, Fauziyyah RZ, Erfanian A (2020) The dominant metric dimension of graphs. Heliyon 6(3):1–6. https://doi.org/10.1016/j.heliyon.2020.e03633

    Article  Google Scholar 

  26. Umilasari R, Susilowati L (2021) Dominant local metric dimension of wheel related graphs. IOP Conf Ser: Mater Sci Eng 1115(1):1–7. https://doi.org/10.1088/1757-899X/1115/1/012029

    Article  Google Scholar 

  27. Lal S, Bhat VK (2022) On the dominant local metric dimension of some planar graphs. Discrete Math Algorithms Appl https://doi.org/10.1142/S179383092250152X

  28. Zuo X, Liu JB, Iqbal H, Ali K, Rizvi ST (2020) Topological indices of certain transformed chemical structures. J Chem https://doi.org/10.1155/2020/3045646

  29. Nadeem MF, Hassan M, Azeem M, Ud-Din Khan S, Shaik MR, Sharaf MA, Abdelgawad A, Awwad EM (2021) Application of resolvability technique to investigate the different polyphenyl structures for polymer industry. J Chem. https://doi.org/10.1155/2021/6633227

  30. Soleimani N, Nikmehr M, Tavallaee H (2015) Computation of the different topological indices of nanostructures. J Natn Sci Foundation Sri Lanka 43(2):127–133. https://doi.org/10.4038/jnsfsr.v43i2.7940

    Article  Google Scholar 

  31. Yang W, Zhang F (2012) Wiener index in random polyphenyl chains. Match Commun Math Comput Chem 68(1):371–376

    Google Scholar 

  32. Huang G, Kuang M, Deng H (2015) Extremal graph with respect to matching energy for a random polyphenyl chain. MATCH Commun Math Comput Chem 73(1):121–131

    Google Scholar 

  33. Ahsan M, Zahid Z, Alrowaili D, Iampan A, Siddique I, Zafar S (2021) On the edge metric dimension of certain polyphenyl chains. J Chem. https://doi.org/10.1155/2021/3855172

  34. Alrowaili D, Zahid Z, Ahsan M, Zafar S, Siddique I (2021) Edge metric dimension of some classes of Toeplitz networks. J Math. https://doi.org/10.1155/2021/3402275

  35. Sardar MS, Siddique I, Jarad F, Ali MA, Türkan EM, Danish M (2022) Computation of vertex-based topological indices of middle graph of alkane \(C_tH_{2t+ 2}\). J Math. https://doi.org/10.1155/2022/8283898

  36. Alam SM, Jarad F, Mahboob A, Siddique I, Altunok T, Waheed Rasheed MA (2022) Survey on generalized topological indices for silicon carbide structure. J Chem https://doi.org/10.1155/2022/7311404

  37. Raza Z, Naz K, Ahmad S (2022) Expected values of molecular descriptors in random polyphenyl chains. Emerg Sci J 6(1):151–165. https://doi.org/10.28991/ESJ-2022-06-01-012

    Article  Google Scholar 

  38. Ke X, Wei S, Huang J (2021) The atom-bond connectivity and geometric-arithmetic indices in random polyphenyl chains. Polycycl Aromat Compd 41(9):1873–1882. https://doi.org/10.1080/10406638.2019.1703763

    Article  CAS  Google Scholar 

  39. Coulson CA (1947) The theory of the structure of free radicals. Discuss Faraday Soc 2:9–18

    Article  Google Scholar 

  40. Coulson CA, Longuet-Higgins HC (1947) The electronic structure of conjugated systems I. General theory. Proc Math Phys Eng Sci 191(1024):39–60. https://doi.org/10.1098/rspa.1947.0102

    Article  CAS  Google Scholar 

  41. Gutman I (1978) Topological formulas for free-valence index. Croat Chem Acta 51(1):29–33

    CAS  Google Scholar 

  42. Villar H, Capparelli AL, Spina A (1986) An HMO extended free valence index and its application. Croat Chem Acta 59(2):383–96

    CAS  Google Scholar 

  43. Kier LB, Hall LH (1997) The E-state as an extended free valence. J Chem Inf Comput Sci 37(3):548–52. https://doi.org/10.1021/ci970002b

    Article  CAS  Google Scholar 

  44. Mayer I (1986) Bond orders and valences from ab initio wave functions. Int J Quantum Chem 29(3):477–83. https://doi.org/10.1002/qua.560290320

    Article  CAS  Google Scholar 

  45. Cooper DL, Ponec R, Karadakov PB (2022) Investigating István Mayer’s “improved” definitions of bond orders and free valence for correlated singlet-state wave functions. Int J Quantum Chem https://doi.org/10.1002/qua.26612

  46. Biggs N, Biggs NL, Norman B (1993) Algebraic graph theory. Cambridge University Press, Cambridge

    Google Scholar 

  47. Roberts JD (1961) Notes on molecular orbital calculations. WA Benjamin Inc, New York

    Google Scholar 

Download references

Acknowledgements

The authors express their sincere thanks to the reviewers for their valuable comments and suggestions that gave the manuscript the present shape.

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SL contributed to conceptualization, visualization, validation, methodology, software, and original draft writing, and VKB contributed to formal analysis, supervision, review, and editing.

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Correspondence to Vijay Kumar Bhat.

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Lal, S., Bhat, V.K. On the dominant local metric dimension of certain polyphenyl chain graphs. Theor Chem Acc 142, 56 (2023). https://doi.org/10.1007/s00214-023-02985-y

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