Modeling trend progression through an extension of the Polya Urn Process
arXiv:1511.01861
Abstract
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.
11 pages, 2 figures, NetSci-X Conference, Wroclaw, Poland, 11-13 January 2016. arXiv admin note: text overlap with arXiv:1502.00166