Scalable Percolation Search in Power Law Networks

We introduce a scalable searching algorithm for finding nodes and contents in random networks with Power-Law (PL) and heavy-tailed degree distributions. The network is searched using a probabilistic broadcast algorithm, where a query message is relayed on each edge with probability just above the bond percolation threshold of the network. We show that if each node caches its directory via a short random walk, then the total number of accessible contents exhibits a first-order phase transition, ensuring very high hit rates just above the percolation threshold. In any random PL network of size, N, and exponent, 2 � τ 0) if kmax = O( p N). Extensive large-scale simulations show these scaling laws to be precise. We discuss how this percolation search algorithm can be directly adapted to solve the wellknown scaling problem in unstructured Peer-to-Peer (P2P) networks. Simulations of the protocol on sample large-scale subnetworks of existing P2P services show that overall traffic can be reduced by almost two-orders of magnitude, without any significant loss in search performance.