Abstract
Sampling via random walks is the first choice for collecting random samples of online-social networks, peer-to-peer networks, and the World Wide Web. This paper proposes an algorithm for random-walk sampling, which allows us to collect a biased (non-random) sample, depending on which nodes are to be investigated in detail. Since the stationary distribution of a random walker under the proposed algorithm can be analytically derived, the bias involved in a collected sample can be removed using the notion of change of measure in probability theory, which is also presented in this paper. The effectiveness of the proposals is verified using simulation experiments based on the data of real networks.
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