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Node sampling using Random Centrifugal Walks
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DOI：10.1016/j.jocs.2015.09.001.png)
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Abstract
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A distributed algorithm is proposed for sampling networks, so that nodes are selected by a special node (source), with a given probability distribution. We define a new class of random walks, that we call Random Centrifugal Walks (ROW). A RCW starts at the source and always moves away from it. The algorithm assumes that each node has a weight, so the nodes are selected with a probability proportional to its weight. It requires a preprocessing phase before the sampling of nodes. This preprocessing is done only once, regardless of the number of sources and the number of samples taken from the network. The length of RCW walks are bounded by the network diameter. The RCW algorithms that do not require preprocessing are proposed for grids and networks with regular concentric connectivity, for the case when the probability of selecting a node is a function of its distance to the source. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Node sampling
Random walks
Randomized algorithms
Distributed algorithms
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