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Homophily in competing behavior spreading among the heterogeneous population with higher-order interactions

Author

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  • Nie, Yanyi
  • Zhong, Xiaoni
  • Lin, Tao
  • Wang, Wei

Abstract

Competing behavior spreading dynamics occur not only through pairwise interactions but also through higher-order collective interactions. The simplicial complex is widely adopted to describe the co-existence of pairwise and higher-order interactions. Previous studies have demonstrated that heterogeneous populations and the homophily effects are crucial in shaping the spreading pattern and phase transition. There is still a lack of a theoretical study for competing spread when higher-order interactions, heterogeneous populations, and homophily effects are all considered at the same time. We propose a mathematical model for the competing behaviors A and B to study the effects of homophily on heterogeneous populations with higher-order interactions. The heterogeneity population consists of three groups. Agents who only adopt behavior A or B are denoted as ΩA and ΩB, respectively. Agents in ΩAB may adopt one of two behaviors. To capture the competing behavior dynamics, we offer a theoretical Microscopic Markov Chain Approach (MMCA). We find that increasing 1-simplex transmission rate contributed to the spread of both two behaviors. The saddle point of the system is investigated and it is shown that the observed coexistence is caused by the average result of multiple experiments, revealing that there is still no coexistence present under our model. Decreasing the proportion of the population ΩAB would lead to a significant decrease in the final adopted density of the system. Due to the existence of groups that only adopt behavior A or B, there are always adopted individuals in the system. In addition, the final adopted density is almost consistent across different homophily effects when the two behaviors interact symmetrically. When the proportion of ΩA remains constant, the final adopted density of behavior A decreases significantly as the proportion of ΩB increases, whereas the final adopted density of behavior B remains almost constant. Also, When the proportion of ΩAB is fixed, an increase in the proportion of population ΩA (ΩB) makes the final adopted density of behavior A (behavior B) to increase with it.

Suggested Citation

  • Nie, Yanyi & Zhong, Xiaoni & Lin, Tao & Wang, Wei, 2022. "Homophily in competing behavior spreading among the heterogeneous population with higher-order interactions," Applied Mathematics and Computation, Elsevier, vol. 432(C).
  • Handle: RePEc:eee:apmaco:v:432:y:2022:i:c:s0096300322004544
    DOI: 10.1016/j.amc.2022.127380
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    References listed on IDEAS

    as
    1. Hu, Liwen & He, Nanrong & Weng, Qifeng & Chen, Xiaojie & Perc, Matjaž, 2020. "Rewarding endowments lead to a win-win in the evolution of public cooperation and the accumulation of common resources," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    2. Li, Hui-Jia & Xu, Wenzhe & Song, Shenpeng & Wang, Wen-Xuan & Perc, Matjaž, 2021. "The dynamics of epidemic spreading on signed networks," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    3. Centola, Damon & Eguíluz, Víctor M. & Macy, Michael W., 2007. "Cascade dynamics of complex propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 449-456.
    4. M E J Newman & Carrie R Ferrario, 2013. "Interacting Epidemics and Coinfection on Contact Networks," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    5. Iacopo Iacopini & Giovanni Petri & Alain Barrat & Vito Latora, 2019. "Simplicial models of social contagion," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    6. Unai Alvarez-Rodriguez & Federico Battiston & Guilherme Ferraz Arruda & Yamir Moreno & Matjaž Perc & Vito Latora, 2021. "Evolutionary dynamics of higher-order interactions in social networks," Nature Human Behaviour, Nature, vol. 5(5), pages 586-595, May.
    7. Rehman, Attiq ul & Singh, Ram & Agarwal, Praveen, 2021. "Modeling, analysis and prediction of new variants of covid-19 and dengue co-infection on complex network," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    8. Li, WenYao & Xue, Xiaoyu & Pan, Liming & Lin, Tao & Wang, Wei, 2022. "Competing spreading dynamics in simplicial complex," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    9. Xiaochen Wang & Yueheng Lan & Jinghua Xiao, 2019. "Anomalous structure and dynamics in news diffusion among heterogeneous individuals," Nature Human Behaviour, Nature, vol. 3(7), pages 709-718, July.
    10. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(4), pages 441-458, May.
    11. Feng Hu & Lin Ma & Xiu-Xiu Zhan & Yinzuo Zhou & Chuang Liu & Haixing Zhao & Zi-Ke Zhang, 2021. "The aging effect in evolving scientific citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4297-4309, May.
    Full references (including those not matched with items on IDEAS)

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