Comparison of Biased and Unbiased Sampling Algorithms Using Graph Metrics

Degree distribution, hierarchy, clustering and small-world are typical graph features to measure the structure of complex networks. This paper uses these features to compare and evaluate the performances of biased and unbiased sampling algorithms on two types of scale-free networks. The numerical analysis verifies that the biased sampling performs better than the unbiased sampling on networks in which high-degree nodes are loosely interconnected. Our work is useful for the in-depth understanding of the biased and unbiased samplings.