Analysis of Web page image tag distribution characteristics

摘要

The authors investigate the frequency distribution of the use of image tags in Web pages. Using data sampled from top level Web pages across five top level domains and from sample pages within individual websites, the authors model observed patterns in the frequency of image tag usage by fitting collected data distributions to different theoretical models used in informetrics. Models tested include the modified power law (MPL), Mandelbrot (MDB), generalized waring (GW), generalized inverse Gaussian–Poisson (GIGP), and generalized negative binomial (GNB) distributions. The GIGP provided the best fit for data sets for top level pages across the top level domains tested. The poor fits of the models to the observed data distributions from specific websites were due to the multimodal nature of the observed data sets. Mixtures of the tested models for the data sets provided better fits. The ability to effectively model Web page attributes, such as the distribution of the number of image tags used per page, is needed for accurate simulation models of Web page content, and makes it possible to estimate the number of requests needed to display the complete content of Web pages.