  Random walks can be used to search a complex networks for a desired resource.
To reduce the number of hops necessary to find the resource, we propose a
search mechanism based on building random walks connecting together partial
walks that have been precomputed at each network node in an initial stage. The
resources found in each partial walk are registered in its associated Bloom
filter. Searches can then jump over partial nodes in which the resource is not
located, significantly reducing search length. However, additional unnecessary
hops come from false positives at the Bloom filters. The analytic model
provided predicts the expected search length of this mechanism, the optimal
size of the partial walks and the corresponding optimal (shortest) expected
search length. Simulation experiments are used to validate these predictions
and to assess the impact of the number of partial walks precomputed in each
node.
