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Network diffusion of competing behaviors

Abstract

Research indicates that network structure affects the diffusion of a single behavior. However, in many social settings, two or more behaviors may compete for adoption, as in the case of religious competition, social movements and counter-movements, or conflicting rumors. Lessons from one-behavior diffusion cannot be easily applied because the outcome can take the form of one-behavior domination, two behaviors splitting the network, both behaviors occupying a small fraction of the network, or no diffusion. This article tests how three well-known factors of single-behavior diffusion—network transitivity, adoption threshold, and connectedness of early adopters—apply to scenarios of competitive diffusion. Results show that minor differences in initial adopter size tend to magnify, creating a significant “head-start advantage.” Nevertheless, the degree of this advantage depends on the interaction between network transitivity, adoption threshold, and connectedness of initial adopters. The article describes the conditions under which countervailing ties may (or may not) create inequality in behavioral diffusion.

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Availability of data and material (data transparency)

Not applicable: this is a simulation study

Code availability

Available in supplementary file.

Notes

  1. I use the term behavior in relation to a wide range of adoption phenomena, such as using technology, wearing a particular fashion item, joining a collective action, and practicing a religion. The key notion is that the behavior is a dichotomous choice: one can either adopt or not adopt.

  2. There are many ways to consider the connectedness of initial adopters. In this article, I connect them as a connected component where all initial adopters are connected as one group through social relationships. Also see section on experiment design.

  3. This is equivalent to “rewiring” the network.

  4. These examples are broadly illustrative rather than strictly true for each case. For instance, although transitivity is generally low in social media networks, it may be high for the social media pages of certain groups where users connect more (e.g., support pages for patients with the same disease).

  5. This refers to the probability of deleting ties from the original network and replacing them with new random ties.

  6. Actors may adopt different behaviors or none at different iterations.

  7. I assume the same threshold and setup of early adopters for both behaviors, rendering the behaviors substitutable.

References

  1. Andrews, K. T. (2002). Movement-countermovement dynamics and the emergence of new institutions: The case of ‘white flight’ schools in mississippi. Social Forces, 80(3), 911–936.

    Article  Google Scholar 

  2. Apt, K. & Evangelos M. 2011. “Diffusion in social networks with competing products.” Pp. 212–23 in Algorithmic Game Theory. Berlin: Springer.

  3. Aral, S., & Van Alstyne, M. (2011). The diversity-bandwidth trade-off. The American Journal of Sociology, 117(1), 90–171.

    Article  Google Scholar 

  4. Baldassarri, D., & Bearman, P. (2007). Dynamics of political polarization. American Sociological Review, 72(5), 784–811.

    Article  Google Scholar 

  5. Barash, V., Cameron, C., & Macy, M. (2012). Critical Phenomena in Complex Contagions. Social Networks, 34(4), 451–461.

    Article  Google Scholar 

  6. Barnett, D., & Njama, K. (1966). Mau mau from within. . New York: Monthly Review Press.

    Google Scholar 

  7. Bennett, W. L., & Segerberg, A. (2012). The logic of connective action: Digital media and the personalization of contentious politics. Information, Communication & Society, 15(5), 739–768.

    Article  Google Scholar 

  8. Berman, B. (1990). Control and crisis in colonial Kenya. . London: James Currey.

    Google Scholar 

  9. Borodin, A., Filmus, Y., & Oren, J. (2010). Threshold models for competitive influence in social networks. In International workshop on internet and network economics (pp. 539-550). Springer, Berlin

  10. Buskens, V., Corten, R., & Weesie, J. (2008). Consent or Conflict: Coevolution of coordination and networks. Journal of Peace Research, 45(2), 205–222.

    Article  Google Scholar 

  11. Burt, R.S. 2001. “Structural holes versus network closure as social capital.” Pp. 31–56 in Social Capital: Theory and Research, edited by N. Lin and K. S. Cook. New Brunswick, NJ: Transaction Publishers.

  12. Centola, D. (2010). The spread of behavior in an online social network experiment. Science, 329(5996), 1194–1197.

    Article  Google Scholar 

  13. Centola, D. 2018. How behavior spreads: The science of complex contagions. Princeton University Press.

  14. Centola, D. (2013). Homophily, networks, and critical mass: Solving the start-up problem in large group collective action. Rationality And Society, 25(1), 3–40.

    Article  Google Scholar 

  15. Centola, D., & Macy, M. (2007). Complex contagions and the weakness of long ties. American Journal of Sociology, 113(3), 702–734.

    Article  Google Scholar 

  16. Centola, D., Willer, R., & Macy, M. (2005). The emperor’s dilemma: A computational model of self-enforcing norms. American Journal of Sociology, 110(4), 1009–1040.

    Article  Google Scholar 

  17. Coleman, J.S., Elihu K., & Herbert M. 1966. Medical innovation: A diffusion study. Bobbs-Merrill Co.

  18. Collins, R. (2012). C-Escalation and D-escalation: A theory of the time-dynamics of conflict. American Sociological Review, 77(1), 1–20.

    Article  Google Scholar 

  19. David, P. A. (1985). Clio and the economics of QWERTY. The American Economic Review, 75(2), 332–337.

    Google Scholar 

  20. Esteban, J., & Ray, D. (2008). Polarization, fractionalization and conflict. Journal of Peace Research, 45(2), 163–182.

    Article  Google Scholar 

  21. Fazeli, A., Ajorlou, A., & Jadbabaie, A. (2017). Competitive diffusion in social networks: quality or seeding? IEEE Transactions on Control of Network Systems, 4(3), 665–675.

    Article  Google Scholar 

  22. Fell, D. (2012). Government and politics in Taiwan. . London: Routledge.

    Book  Google Scholar 

  23. Frederiks, M. (2010). Let us understand our differences: Current trends in christian-muslim relations in Sub-Saharan Africa. Transformation Groups, 27(4), 261–274.

    Article  Google Scholar 

  24. González-Bailón, S., Borge-Holthoefer, J., Alejandro R., & Yamir M. 2011. “The Dynamics of Protest Recruitment through an Online Network.” Scientific Reports 197

  25. Gould, R. V. (1991). Multiple networks and mobilization in the paris commune, 1871. American Sociological Review, 56(6), 716–729.

    Article  Google Scholar 

  26. Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.

    Article  Google Scholar 

  27. Granovetter, M. (1978). Threshold models of collective behavior. The American Journal of Sociology, 83(6), 1420–1443.

    Article  Google Scholar 

  28. Heckathorn, D. D. (1993). Collective action and group heterogeneity: Voluntary provision versus selective incentives. American Sociological Review, 58(3), 329–350.

    Article  Google Scholar 

  29. Heckathorn, D. D. (1996). The dynamics and dilemmas of collective action. American Sociological Review, 61(2), 250–277.

    Article  Google Scholar 

  30. Hsiao, Y. 2017. “Virtual ecologies, mobilization and democratic groups without leaders: Impacts of internet media on the wild strawberry movement.” Pp. 50–69 in Taiwan’s social movements under Ma Ying-jeou. London: Routledge.

  31. Hu, H. (2017). Competing opinion diffusion on social networks. Royal Society Open Science, 4(11), 171160.

    Article  Google Scholar 

  32. Iannaccone, L. R. (1991). The consequences of religious market structure: Adam smith and the economics of religion. Rationality And Society, 3(2), 156–177.

    Article  Google Scholar 

  33. Johnson, C. (1962). Peasant nationalism and communist power: The emergence of revolutionary China, 1937–1945. Stanford University Press.

  34. Kalyvas, S. N. (2006). The logic of violence in civil war. Cambridge University Press.

  35. Kim, H., & Bearman, P. S. (1997). The structure and dynamics of movement participation. American Sociological Review, 62(1), 70–93.

    Article  Google Scholar 

  36. Kim, H., & Pfaff, S. (2012). Structure and dynamics of religious insurgency: students and the spread of the reformation. American Sociological Review, 77(2), 188–215.

    Article  Google Scholar 

  37. Kim, H., Rhee, E.-Y., & Yee, J. (2008). Comparing fashion process networks and friendship networks in small groups of adolescents. Journal of Fashion Marketing and Management, 12(4), 545–564.

    Article  Google Scholar 

  38. Kitts, J. (2000). Mobilizing in black boxes: Social Networks and participation in social movement organizations. Mobilization, 5(2), 241–257.

    Article  Google Scholar 

  39. Kitts, J.A. & Yongren S. 2018. “Toward an analytical framework of social influence: Behavioral diffusion.” National Academy of Sciences, Exploring the Development of Analytic Frameworks.

  40. Macy, M. W. (1990). Learning theory and the logic of critical mass. American Sociological Review, 55(6), 809–826.

    Article  Google Scholar 

  41. Mahmood, S. (2012). Religious freedom, the minority question, and geopolitics in the middle east. Comparative Studies in Society and History, 54(2), 418–446.

    Article  Google Scholar 

  42. Mahoney, J. (2000). Path dependence in historical sociology. Theory and Society, 29(4), 507–548.

    Article  Google Scholar 

  43. Marwell, G., & Oliver, P. (1993). The critical mass in collective action. . New York: Cambridge University Press.

    Book  Google Scholar 

  44. Marwell, G., Oliver, P. E., & Prahl, R. (1988). Social networks and collective action: A theory of the critical mass. III. American Journal of Sociology, 94(3), 502–534.

    Article  Google Scholar 

  45. McPherson, M. (1983). An ecology of affiliation. American Sociological Review, 48(4), 519–532.

    Article  Google Scholar 

  46. McPherson, M. (2004). A blau space primer: Prolegomenon to an ecology of affiliation. Industrial and Corporate Change, 13(1), 263–280.

    Article  Google Scholar 

  47. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: homophily in social networks. Annual Review of Sociology, 27(1), 415–444.

    Article  Google Scholar 

  48. McVeigh, R., Cunningham, D., & Farrell, J. (2014). Political polarization as a social movement outcome: 1960s klan activism and its enduring impact on political realignment in southern counties, 1960 to 2000. American Sociological Review, 79(6), 1144–1171.

    Article  Google Scholar 

  49. Mercea, D. (2013). Probing the implications of Facebook use for the organizational form of social movement organizations. Information, Communication & Society, 16(8), 1306–1327.

    Article  Google Scholar 

  50. Montalvo, J. G., & Reynal-Querol, M. (2005). Ethnic polarization, potential conflict, and civil wars. The American Economic Review, 95(3), 796–816.

    Article  Google Scholar 

  51. Morris, A. D. (1984). The origins of the civil rights movement. . New York: The Free Press.

    Google Scholar 

  52. Newman, M. E. J., & Watts, D. J. (1999). Renormalization group analysis of the small-world network model. Physics Letters. A, 263(4), 341–346.

    Article  Google Scholar 

  53. Oliver, P., Marwell, G., & Teixeira, R. (1985). A theory of the critical mass. I. Interdependence, group heterogeneity, and the production of collective action. American Journal of Sociology, 91(3), 522–556.

    Article  Google Scholar 

  54. Pariser, E. (2011). The filter bubble: What the internet is hiding from you. . New York: Penguin.

    Google Scholar 

  55. Petersen, A. M., Jung, W.-S., Yang, J.-S., & Eugene Stanley, H. (2011). Quantitative and empirical demonstration of the matthew effect in a study of career longevity. Proceedings of the National Academy of Sciences, 108(1), 18–23.

    Article  Google Scholar 

  56. Rogers, E.M. 1995. “Diffusion of innovations: Modifications of a model for telecommunications.” Pp. 25–38 in Die Diffusion von Innovationen in der Telekommunikation, edited by M.-W. Stoetzer and A. Mahler. Berlin: Springer.

  57. Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854–856.

    Article  Google Scholar 

  58. Schelling, T. C. (1978). Micromotives and macrobehavior. . New York: Norton.

    Google Scholar 

  59. Siegel, D. A. (2009). Social networks and collective action. American Journal of Political Science, 53(1), 122–138.

    Article  Google Scholar 

  60. Soule, S. A., & King, B. G. (2008). Competition and resource partitioning in three social movement industries. American Journal of Sociology, 113(6), 1568–1610.

    Article  Google Scholar 

  61. Taylor, D. G., Lewin, J. E., & Strutton, D. (2011). Friends, fans, and followers: Do ads work on social networks? How gender and age shape receptivity. Journal of Advertising Research, 51(1), 258–275.

    Article  Google Scholar 

  62. Thomas, S. A. (2007). Lies, damn lies, and rumors: An analysis of collective efficacy, rumors, and fear in the wake of Katrina. Sociological Spectrum, 27(6), 679–703.

    Article  Google Scholar 

  63. Valente, T. W. (1996). Social network thresholds in the diffusion of innovations. Social Networks, 18(1), 69–89.

    Article  Google Scholar 

  64. Vise, David. 2007. “The Google Story.” Strategic Direction.

  65. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. . New York: Cambridge University Press.

    Book  Google Scholar 

  66. Watts, D. J. (1999). Networks, dynamics, and the small-world phenomenon. American Journal of Sociology, 105(2), 493–527.

    Article  Google Scholar 

  67. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.

    Article  Google Scholar 

  68. Wimmer, A. (2003). Democracy and Ethno-Religious conflict in Iraq. Survival, 45(4), 111–134.

    Article  Google Scholar 

  69. Wojcieszak, M. (2010). ‘Don’t talk to me’: Effects of ideologically homogeneous online groups and politically dissimilar offline ties on extremism. New Media & Society, 12(4), 637–655.

    Article  Google Scholar 

  70. Wood, E. J. (2003). Insurgent collective action and civil war in El salvador. Cambridge University Press.

  71. Zhang, J. & Damon C. 2019. Social networks and health: new developments in diffusion, online and offline. Annual Review of Sociology.

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Hsiao, Y. Network diffusion of competing behaviors. J Comput Soc Sc 5, 47–68 (2022). https://doi.org/10.1007/s42001-021-00115-x

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Keywords

  • Competitive diffusion
  • Behavioral adoption
  • Social networks
  • Simulations