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Effects of quadrilateral clustering on complex contagion

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  • Jeong, Wonhee
  • Yu, Unjong

Abstract

Clustering is one of the most important properties that determine the function of complex networks. But the conventional clustering coefficient considers only triangles without a clear basis. To examine the role of higher-order clustering beyond the conventional triangular clustering, we propose the quadrilateral clustering coefficient that counts the number of cycles of length 4. We also present algorithms to generate quadrilateral clustered networks with regular and scale-free degree distributions. We study the complex contagion model, where clustering promotes spreading. We show that quadrilateral clustered networks have a significant clustering effect, despite negligible conventional clustering coefficient. Moreover, we demonstrate that the clustering effect is stronger in the square lattice with zero conventional clustering coefficient than in the kagome lattice with a sizable conventional clustering coefficient, counterintuitively. Therefore, we conclude that the clustering by quadrilaterals is critical as well as the classical triangular clustering at least in complex contagion.

Suggested Citation

  • Jeong, Wonhee & Yu, Unjong, 2022. "Effects of quadrilateral clustering on complex contagion," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
  • Handle: RePEc:eee:chsofr:v:165:y:2022:i:p1:s0960077922009638
    DOI: 10.1016/j.chaos.2022.112784
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    1. Petra M. Gleiss & Peter F. Stadler & Andreas Wagner & David A. Fell, 2001. "Relevant Cycles In Chemical Reaction Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 4(02n03), pages 207-226.
    2. G. Caldarelli & R. Pastor-Satorras & A. Vespignani, 2004. "Structure of cycles and local ordering in complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 38(2), pages 183-186, March.
    3. Badham, Jennifer & Stocker, Rob, 2010. "The impact of network clustering and assortativity on epidemic behaviour," Theoretical Population Biology, Elsevier, vol. 77(1), pages 71-75.
    4. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    5. Tahir Khan & Zi-Shan Qian & Roman Ullah & Basem Al Alwan & Gul Zaman & Qasem M. Al-Mdallal & Youssef El Khatib & Khaled Kheder & Mustafa Cagri Kutlu, 2021. "The Transmission Dynamics of Hepatitis B Virus via the Fractional-Order Epidemiological Model," Complexity, Hindawi, vol. 2021, pages 1-18, December.
    6. Erez Lieberman & Christoph Hauert & Martin A. Nowak, 2005. "Evolutionary dynamics on graphs," Nature, Nature, vol. 433(7023), pages 312-316, January.
    7. Chang, Sheryl L. & Piraveenan, Mahendra & Prokopenko, Mikhail, 2020. "Impact of network assortativity on epidemic and vaccination behaviour," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    8. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-440, June.
    9. Choi, Jeong-Ok & Yu, Unjong, 2020. "Diffusion of innovations in finite networks: Effects of heterogeneity, clustering, and bilingual option on the threshold in the contagion game model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    10. Francisco C. Santos & Marta D. Santos & Jorge M. Pacheco, 2008. "Social diversity promotes the emergence of cooperation in public goods games," Nature, Nature, vol. 454(7201), pages 213-216, July.
    11. Choi, Jeong-Ok & Yu, Unjong, 2018. "Fixation probability on clique-based graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 2129-2135.
    12. Muhammad Umar & Kusen & Muhammad Asif Zahoor Raja & Zulqurnain Sabir & Qasem Al-Mdallal, 2022. "A computational framework to solve the nonlinear dengue fever SIR system," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 25(16), pages 1821-1834, December.
    13. Lingling Xia & Guoping Jiang & Yurong Song & Bo Song, 2017. "An improved local immunization strategy for scale-free networks with a high degree of clustering," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 90(1), pages 1-7, January.
    14. Fronczak, Agata & Hołyst, Janusz A & Jedynak, Maciej & Sienkiewicz, Julian, 2002. "Higher order clustering coefficients in Barabási–Albert networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 688-694.
    15. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    16. Wonhee Jeong & Tarik Hadzibeganovic & Unjong Yu, 2022. "Evolution of cooperation with time-varying tags and heterogeneous immigration dynamics," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 33(12), pages 1-26, December.
    17. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331, October.
    18. Eocman Lee & Jeho Lee & Jongseok Lee, 2006. "Reconsideration of the Winner-Take-All Hypothesis: Complex Networks and Local Bias," Management Science, INFORMS, vol. 52(12), pages 1838-1848, December.
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