Network navigation using Page Rank random walks
This work provides incremental insights into network navigation models, linking Page Rank to Lévy walks for researchers in network theory and search algorithms.
The authors analyzed Page Rank random walks using a continuous time approximation, finding that the occupancy probability dynamics exponentially forget initial conditions and reach a steady state dependent on network characteristics, with the average transit time between nodes showing connections to Lévy walks.
We introduce a formalism based on a continuous time approximation, to study the characteristics of Page Rank random walks. We find that the diffusion of the occupancy probability has a dynamics that exponentially "forgets" the initial conditions and settles to a steady state that depends only on the characteristics of the network. In the special case in which the walk begins from a single node, we find that the largest eigenvalue of the transition value (lambda=1) does not contribute to the dynamic and that the probability is constant in the direction of the corresponding eigenvector. We study the process of visiting new node, which we find to have a dynamic similar to that of the occupancy probability. Finally, we determine the average transit time between nodes <T>, which we find to exhibit certain connection with the corresponding time for Levy walks. The relevance of these results reside in that Page Rank, which are a more reasonable model for the searching behavior of individuals, can be shown to exhibit features similar to Levy walks, which in turn have been shown to be a reasonable model of a common large scale search strategy known as "Area Restricted Search".