Abstract
In many social system the interactions among the individuals are rapidly changing and are characterized with timing. The dynamics of social interaction constantly affects the development of their opinions. However, most of the opinion evolution models characterize interpersonal opinion in static, structural properties of the network such as degree, cluster and distance. In this paper, an Deffuant opinion model based on the activity-driven network is developed to examine how different activity distribution effects the dynamics of opinion evolution. When the activity distribution complies with power-law distribution or random distribution, phase transition transform from polarization to consensus when threshold is 0.6 and 0.4, respectively. In the process of opinion formation the distribution of opinion clusters’ scales are complying with power-law distribution. Especially, under the power-law distribution the opinion disparity of the two clusters in polarization state is lower than the others, which means that the burst of the activity helps the individuals converging in opinion clusters in values. Finally we show that the speed to reach stable is influenced by the type of activity distribution. The simulation on power-distribution and random distribution need more time steps to get steady state.
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This work was supported in part by the National Natural Science Foundation (grant numbers 71401024, 71371040 and 71801145).
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Zhang, J., Xia, H., Li, P. (2018). Dynamics of Deffuant Model in Activity-Driven Online Social Network. In: Chen, J., Yamada, Y., Ryoke, M., Tang, X. (eds) Knowledge and Systems Sciences. KSS 2018. Communications in Computer and Information Science, vol 949. Springer, Singapore. https://doi.org/10.1007/978-981-13-3149-7_16
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DOI: https://doi.org/10.1007/978-981-13-3149-7_16
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