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Network structure construction and global state maintenance are expensive in large-scale, dynamic peer-to-peer (p2p) networks. With inherent topology independence and low state maintenance overhead, random walk is an excellent tool in such network environments. However, the current uses are limited to unguided or heuristic random walks with no guarantee on their converged node visitation probability distribution. Such a convergence guarantee is essential for strong analytical properties and high performance of many p2p applications. In this paper, the authors investigate an approach for random walks to converge to application-desired node visitation probability distributions while only requiring information about direct neighbors of each peer.
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