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Modeling trend progression through an extension of the Polya Urn Process

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.

preprint2015arXivOpen access
Marijn ten ThijSandjai Bhulai
Social and Information Networksphysics.soc-phmath.PR
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Marijn ten ThijSandjai Bhulai

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