Exploring complex networks by walking on them

TL;DR

This paper compares search strategies on complex networks, finding that self-avoid random walks are most efficient without global knowledge, and suggests that search results can reveal network topology.

Contribution

It provides a comparative analysis of search strategies on complex networks and identifies the self-avoid random walk as the most effective without global information.

Findings

Self-avoid random walk is the most efficient search strategy.

Preferential self-avoidance does not improve search efficiency.

Search results can infer network topological features.

Abstract

We carry out a comparative study on the problem for a walker searching on several typical complex networks. The search efficiency is evaluated for various strategies. Having no knowledge of the global properties of the underlying networks and the optimal path between any two given nodes, it is found that the best search strategy is the self-avoid random walk. The preferentially self-avoid random walk does not help in improving the search efficiency further. In return, topological information of the underlying networks may be drawn by comparing the results of the different search strategies.