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Random walks can be used to search complex networks for a desired resource.To reduce search lengths, we propose a mechanism based on building random walksconnecting together partial walks (PW) previously computed at each networknode. Resources found in each PW are registered. Searches can then jump overPWs where the resource is not located. However, we assume that perfectrecording of resources may be costly, and hence, probabilistic structures likeBloom filters are used. Then, unnecessary hops may come from false positives atthe Bloom filters. Two variations of this mechanism have been considered,depending on whether we first choose a PW in the current node and then check itfor the resource, or we first check all PWs and then choose one. In addition,PWs can be either simple random walks or self-avoiding random walks. Analyticalmodels are provided to predict expected search lengths and other magnitudes ofthe resulting four mechanisms. Simulation experiments validate thesepredictions and allow us to compare these techniques with simple random walksearches, finding very large reductions of expected search lengths.

Improving Resource Location with Locally Precomputed Partial Random Walks