A New Relational Networks Sampling Algorithm Using Topologically Divided Stratums

One popular solution to deal with large-scale relational networks is to derive a representative sample from huge relational networks. We expect this sample could represent the origin relational network well so that the sampled network can be used for simulations and further analysis instead of the origin one. In this paper, we propose a network stratified sampling algorithm using topologically divided stratums for large relational networks, which can maintain the topological similarity well between sampled network and original network. In addition, we evaluate our algorithm on several well-known data sets. The experimental results show that our algorithm outperforms the previous methods.