The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Continuous-time model of structural balance
Open accessAbstract
It is not uncommon for certain social networks to divide into two opposing camps in response to stress. This happens, for example, in networks of political parties during winner-takes-all elections, in networks of companies competing to establish technical standards, and in networks of nations faced with mounting threats of war. A simple model for these two-sided separations is the dynamical system dX / dt = X 2 , where X is a matrix of the friendliness or unfriendliness between pairs of nodes in the network. Previous simulations suggested that only two types of behavior were possible for this system: Either all relationships become friendly or two hostile factions emerge. Here we prove that for generic initial conditions, these are indeed the only possible outcomes. Our analysis yields a closed-form expression for faction membership as a function of the initial conditions and implies that the initial amount of friendliness in large social networks (started from random initial conditions) determines whether they will end up in intractable conflict or global harmony.
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Cites
A meta-analytic test of intergroup contact theory.
Pettigrew, T. F., & Tropp, L. R. (2006) · Journal of personality and social psychology · doi:10.1037/0022-3514.90.5.751 · Cited by 9,708
Social psychology of intergroup relations
Tajfel, H. (1982) · Annual Review of Psychology · doi:10.1146/annurev.ps.33.020182.000245 · Cited by 7,949
Pettigrew, T. F. (1998) · Annual Review of Psychology · doi:10.1146/annurev.psych.49.1.65 · Cited by 6,001
What We Know Now About Bias and Intergroup Conflict, the Problem of the Century
Fiske, S. T. (2002) · Current Directions in Psychological Science · doi:10.1111/1467-8721.00183 · Cited by 384
Cited by
Dynamic social networks promote cooperation in experiments with humans
Rand, D. G., Arbesman, S., & Christakis, N. A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1108243108 · Cited by 653
A tutorial on modeling and analysis of dynamic social networks. Part I
Proskurnikov, A. V., & Tempo, R. (2017) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2017.03.002 · Cited by 553
What are higher-order networks?
Bick, C., Gross, E., Harrington, H. A., & Schaub, M. T. (2023) · SIAM Review · doi:10.1137/21m1414024 · Cited by 420
Opinion Dynamics in Social Networks With Hostile Camps: Consensus vs. Polarization
Proskurnikov, A. V., Matveev, A. S., & Cao, M. (2016) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2015.2471655 · Cited by 357
Opinion Dynamics and the Evolution of Social Power in Influence Networks
Jia, P., MirTabatabaei, A., Friedkin, N. E., & Bullo, F. (2015) · SIAM Review · doi:10.1137/130913250 · Cited by 318
Metadata
- Authors
- Marvel, S. A., Kleinberg, J., Kleinberg, R. D., & Strogatz, S. H.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2011-01-03
- Volume
- 108
- Issue
- 5
- Pages
- 1771-1776
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2046643693
- Cited by
- 298
- References
- 4
- FWCI
- 11.20
- Type
- journal article
- Language
- en
- Open access
- green
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