Exploring complex networks by means of adaptive walkers

TL;DR

This paper introduces an adaptive random walk model for exploring large networks, optimizing information retrieval about network structure by balancing exploration and return strategies.

Contribution

It proposes a novel adaptive strategy for random walkers that improves network exploration efficiency and network reconstruction accuracy.

Findings

Optimal exploration strategies depend on home node degree

Adaptive walkers outperform non-adaptive methods

Enhanced network reconstruction accuracy achieved

Abstract

Finding efficient algorithms to explore large networks with the aim of recovering information about their structure is an open problem. Here, we investigate this challenge by proposing a model in which random walkers with previously assigned home nodes navigate through the network during a fixed amount of time. We consider that the exploration is successful if the walker gets the information gathered back home, otherwise, no data is retrieved. Consequently, at each time step, the walkers, with some probability, have the choice to either go backward approaching their home or go farther away. We show that there is an optimal solution to this problem in terms of the average information retrieved and the degree of the home nodes and design an adaptive strategy based on the behavior of the random walker. Finally, we compare different strategies that emerge from the model in the context of network reconstruction. Our results could be useful for the discovery of unknown connections in large scale networks.