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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 throughthe network. The context of the trend is not included in our model. We showthat every state of the RG model maps to a state of the Polya process. We findthat the limit of the component size distribution of the RG model showspower-law behaviour. These results are also supported by simulations.

Modeling trend progression through an extension of the Polya Urn Process