Knowing how and when trends are formed is a frequently visited research goal. In our work, we focus on the progression of trends through (social) networks. We use a random graph (RG) model to mimic the progression of a trend through the network. The context of the trend is not included in our model. We show that every state of the RG model maps to a state of the Polya process. We find that the limit of the component size distribution of the RG model shows power-law behaviour. These results are also supported by simulations.
CITATION STYLE
ten Thij, M., & Bhulai, S. (2016). Modelling trend progression through an extension of the Polya urn process. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9564, pp. 57–67). Springer Verlag. https://doi.org/10.1007/978-3-319-28361-6_5
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