IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v432y2022ics0096300322004544.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300322004544
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2022.127380?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. 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).
    4. 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.
    5. 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.
    6. 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.
    7. Iacopo Iacopini & Giovanni Petri & Alain Barrat & Vito Latora, 2019. "Simplicial models of social contagion," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ma, Ning & Yu, Guang & Jin, Xin, 2024. "Dynamics of competing public sentiment contagion in social networks incorporating higher-order interactions during the dissemination of public opinion," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    2. Tian, Yang & Tian, Hui & Cui, Yajuan & Zhu, Xuzhen & Cui, Qimei, 2023. "Influence of behavioral adoption preference based on heterogeneous population on multiple weighted networks," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    3. Zhe-Fei Mao & Qi-Wei Li & Yi-Ming Wang & Jie Zhou, 2024. "Pro-religion attitude predicts lower vaccination coverage at country level," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
    4. Mariana Azevedo & Paulo Reis Mourão, 2023. "The evolution of epidemics and the publication of epidemic news in the local press: a study in the region of Braga (Northern Portugal)," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    5. Wu, Qingchu & Kabir, K.M. Ariful, 2023. "Compact pairwise methods for susceptible–infected–susceptible epidemics on weighted heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    6. You, Xuemei & Zhang, Man & Ma, Yinghong & Tan, Jipeng & Liu, Zhiyuan, 2023. "Impact of higher-order interactions and individual emotional heterogeneity on information-disease coupled dynamics in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    7. Youjia Zhou & Chen Dong, 2023. "Nourishing social solidarity in exchanging gifts: a study on social exchange in Shanghai communities during COVID-19 lockdown," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Wang, Wei & Li, Wenyao & Lin, Tao & Wu, Tao & Pan, Liming & Liu, Yanbing, 2022. "Generalized k-core percolation on higher-order dependent networks," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    3. Nie, Yanyi & Li, Wenyao & Pan, Liming & Lin, Tao & Wang, Wei, 2022. "Markovian approach to tackle competing pathogens in simplicial complex," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    4. Ma, Ning & Yu, Guang & Jin, Xin, 2024. "Dynamics of competing public sentiment contagion in social networks incorporating higher-order interactions during the dissemination of public opinion," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    5. Zhao, Dandan & Li, Runchao & Peng, Hao & Zhong, Ming & Wang, Wei, 2022. "Higher-order percolation in simplicial complexes," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    6. Zhao, Dandan & Li, Runchao & Peng, Hao & Zhong, Ming & Wang, Wei, 2022. "Percolation on simplicial complexes," Applied Mathematics and Computation, Elsevier, vol. 431(C).
    7. Zhang, Ziyu & Mei, Xuehui & Jiang, Haijun & Luo, Xupeng & Xia, Yang, 2023. "Dynamical analysis of Hyper-SIR rumor spreading model," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    8. Li, Xueqi & Ghosh, Dibakar & Lei, Youming, 2023. "Chimera states in coupled pendulum with higher-order interaction," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    9. Luca Gallo & Lucas Lacasa & Vito Latora & Federico Battiston, 2024. "Higher-order correlations reveal complex memory in temporal hypergraphs," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    10. Guilherme Ferraz de Arruda & Giovanni Petri & Pablo Martin Rodriguez & Yamir Moreno, 2023. "Multistability, intermittency, and hybrid transitions in social contagion models on hypergraphs," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    11. Wang, Xuhui & Wu, Jiao & Yang, Zheng & Xu, Kesheng & Wang, Zhengling & Zheng, Muhua, 2024. "The correlation between independent edge and triangle degrees promote the explosive information spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    12. Nie, Yanyi & Zhong, Xiaoni & Lin, Tao & Wang, Wei, 2023. "Pathogen diversity in meta-population networks," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    13. Li, Wenyao & Cai, Meng & Zhong, Xiaoni & Liu, Yanbing & Lin, Tao & Wang, Wei, 2023. "Coevolution of epidemic and infodemic on higher-order networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    14. Martina Contisciani & Federico Battiston & Caterina De Bacco, 2022. "Inference of hyperedges and overlapping communities in hypergraphs," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    15. Ramasamy, Mohanasubha & Devarajan, Subhasri & Kumarasamy, Suresh & Rajagopal, Karthikeyan, 2022. "Effect of higher-order interactions on synchronization of neuron models with electromagnetic induction," Applied Mathematics and Computation, Elsevier, vol. 434(C).
    16. Lv, Xijian & Fan, Dongmei & Yang, Junxian & Li, Qiang & Zhou, Li, 2024. "Delay differential equation modeling of social contagion with higher-order interactions," Applied Mathematics and Computation, Elsevier, vol. 466(C).
    17. Ma, Jinlong & Wang, Peng, 2024. "Impact of community networks with higher-order interaction on epidemic dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    18. Song, Shenpeng & Feng, Yuhao & Xu, Wenzhe & Li, Hui-Jia & Wang, Zhen, 2022. "Evolutionary prisoner’s dilemma game on signed networks based on structural balance theory," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    19. Iacopo Iacopini & Márton Karsai & Alain Barrat, 2024. "The temporal dynamics of group interactions in higher-order social networks," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    20. Teruyoshi Kobayashi, 2015. "Trend-driven information cascades on random networks," Discussion Papers 1529, Graduate School of Economics, Kobe University.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:432:y:2022:i:c:s0096300322004544. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.