Journal of Data Science

Abstract: Modeling the Internet has been an active research in the past ten years. From the “rich get richer” behavior to the “winners don’t take all” property, the models depend on the explicit attributes described in the net work. This paper discusses the modeling of non-scale-free network subsets like bulletin forums. A new evolution mechanism, driven by some implicit at tributes “hidden” in the network, leads to a slightly increase in the page sizes of front rank forum. Due to the complication of quantifying these implicit attributes, two potential models are suggested. The first model introduces a content ratio and it is patched to the lognormal model, while the second model truncates the data into groups according to their regional specialties and data within groups are fitted by power-law models. A Taiwan-based bulletin forum is used for illustration and data are fitted via four models. Statistical Diagnostics show that two suggested models perform better than the traditional models in data fitting and predictions. In particular, the second model performs better than the first model in general.