Scaling of human behavior during portal browsing

TL;DR

This study analyzes user navigation patterns across portal subpages, revealing power-law distributions, non-linear node strength growth, and self-attracting walk behavior, supported by an analytical model.

Contribution

It introduces a weighted network model of portal browsing, uncovering scaling laws and dynamic behaviors of user navigation not previously characterized.

Findings

Link weights and node strengths follow power-law distributions.

Node strength grows faster than linearly with node degree.

User paths resemble self-attracting walks on the network.

Abstract

We investigate transitions of portals users between different subpages. A weighted network of portals subpages is reconstructed where edge weights are numbers of corresponding transitions. Distributions of link weights and node strengths follow power laws over several decades. Node strength increases faster than linearly with node degree. The distribution of time spent by the user at one subpage decays as power law with exponent around 1.3. Distribution of numbers P(z) of unique subpages during one visit is exponential. We find a square root dependence between the average z and the total number of transitions n during a single visit. Individual path of portal user resembles of self-attracting walk on the weighted network. Analytical model is developed to recover in part the collected data.