A model of the effects of authority on consensus formation in adaptive networks: Impact on network topology and robustness
AbstractOpinions of individuals in real social networks are arguably strongly influenced by external determinants, such as the opinions of those perceived to have the highest levels of authority. In order to model this, we have extended an existing model of consensus formation in an adaptive network by the introduction of a parameter representing each agent’s level of ‘authority’, based on their opinion relative to the overall opinion distribution. We found that introducing this model, along with a randomly varying opinion convergence factor, significantly impacts the final state of converged opinions and the number of interactions required to reach that state. We also determined the relationship between initial and final network topologies for this model, and whether the final topology is robust to node removals. Our results indicate firstly that the process of consensus formation with a model of authority consistently transforms the network from an arbitrary initial topology to one with distinct measurements in mean shortest path, clustering coefficient, and degree distribution. Secondly, we found that subsequent to the consensus formation process, the mean shortest path and clustering coefficient are less affected by both random and targeted node disconnection. Speculation on the relevance of these results to real world applications is provided.
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Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 392 (2013)
Issue (Month): 4 ()
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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/
Social networks; Opinion dynamics; Consensus formation; Complex network; Network robustness; Directed networks; Sociopsychology; Human dynamics; Bounded confidence model;
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- Zhang, Zhongzhi & Rong, Lili & Comellas, Francesc, 2006. "High-dimensional random Apollonian networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 610-618.
- Amblard, Frédéric & Deffuant, Guillaume, 2004. "The role of network topology on extremism propagation with the relative agreement opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 343(C), pages 725-738.
- A. Barrat & M. Weigt, 2000. "On the properties of small-world network models," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 13(3), pages 547-560, 02.
- Weisbuch, Gérard & Deffuant, Guillaume & Amblard, Frédéric, 2005. "Persuasion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 555-575.
- Guillaume Deffuant & Frederic Amblard & G�rard Weisbuch, 2002. "How Can Extremism Prevail? a Study Based on the Relative Agreement Interaction Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1.
- Gandica, Yérali & del Castillo-Mussot, Marcelo & Vázquez, Gerardo J. & Rojas, Sergio, 2010. "Continuous opinion model in small-world directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5864-5870.
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