Blockchain, which is a technology for distributedly managing ledger information over multiple nodes without a centralized system, has elicited increasing attention. Performing experiments on actual blockchains is difficult because a large number of nodes in wide areas are necessary. In this study, we developed a blockchain network simulator SimBlock for such experiments. Unlike the existing simulators, SimBlock can easily change behavior of nodes, so that it enables to investigate the influence of nodes' behavior on blockchains. We compared some simulation results with the measured values in actual blockchains to demonstrate the validity of this simulator Furthermore, to show practical usage, we conducted two experiments which clarify the influence of neighbor node selection algorithms and relay networks on the block propagation time. The simulator could depict the effects of the two techniques on block propagation time. The simulator will be publicly available in a few months.
Accurately analyzing graph properties of social networks is a challenging task because of access limitations to the graph data. To address this challenge, several algorithms to obtain unbiased estimates of properties from few samples via a random walk have been studied. However, existing algorithms do not consider private nodes who hide their neighbors in real social networks, leading to some practical problems. Here we design random walk-based algorithms to accurately estimate properties without any problems caused by private nodes. First, we design a random walk-based sampling algorithm that comprises the neighbor selection to obtain samples having the Markov property and the calculation of weights for each sample to correct the sampling bias. Further, for two graph property estimators, we propose the weighting methods to reduce not only the sampling bias but also estimation errors due to private nodes. The proposed algorithms improve the estimation accuracy of the existing algorithms by up to 92.6% on real-world datasets.
CCS CONCEPTS• General and reference → Estimation; • Mathematics of computing → Graph algorithms.
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