How do power laws explain web navigation behaviours?

Power laws explain web navigation behaviours by showing that a few pages receive most visits while many get few or none.

Power laws, also known as Pareto distributions or Zipf's law, are mathematical relationships found in many natural and human-made systems. In the context of web navigation, they describe the distribution of page visits: a small number of pages receive a disproportionately large number of visits, while a large number of pages receive very few or no visits at all. This is often visualised as a 'long tail' graph, where the 'head' represents the popular pages and the 'tail' represents the less visited ones.

The power law distribution emerges from the way users navigate the web. Users tend to start their navigation from a few popular pages (like Google or Facebook) and then follow links to other pages. This process is known as 'preferential attachment', as users prefer to attach to popular pages. Over time, this leads to a reinforcement effect where popular pages become more popular, creating a 'rich-get-richer' dynamic.

Moreover, the power law distribution is not static but evolves over time. New pages can become popular and old pages can lose their popularity. This dynamic nature of web navigation is captured by the concept of 'fitness', which measures a page's ability to attract and retain visits. Pages with high fitness can climb the popularity rank and break the 'rich-get-richer' dynamic.

In summary, power laws provide a mathematical model to understand and predict web navigation behaviours. They capture the uneven distribution of page visits, the preferential attachment process, and the dynamic nature of web popularity. Understanding these aspects is crucial for web designers, online marketers, and search engine optimisers, as it can help them to design more effective web pages, to target their audience more accurately, and to improve their search engine ranking.