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Estimating clustering coefficients via metropolis-hastings random walk and wedge sampling on large OSN graphs

We propose estimators for popular clustering coefficient measures 1) network average clustering coefficient and 2) global clustering coefficient (aka transitivity). Unlike most of previous studies estimating clustering coefficients, we do not use independent vertex sampling as it is either unavailable or inefficient to implement in most Online Social Networks (OSNs). Instead, we propose estimators based on Metropolis-Hastings Random Walk (MHRW) and wedge sampling. We use several large scale OSN graphs to compare the accuracy of the proposed estimators with that of the existing alternatives. The evaluation results show that our estimator for transitivity generally outperforms the alternative RW-based estimator in most graphs. However, in general, the underlying graph structure and the studied characteristic has important effect in the estimation accuracy.