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Graph sampling via crawling has become increasingly popular and important inthe study of measuring various characteristics of large scale complex networks.While powerful, it is known to be challenging when the graph is looselyconnected or disconnected which slows down the convergence of random walks andcan cause poor estimation accuracy.  In this work, we observe that the graph under study, or called target graph,usually does not exist in isolation. In many situations, the target graph isrelated to an auxiliary graph and an affiliation graph, and the target graphbecomes well connected when we view it from the perspective of these threegraphs together, or called a hybrid social-affiliation graph in this paper.When directly sampling the target graph is difficult or inefficient, we canindirectly sample it efficiently with the assistances of the other two graphs.We design three sampling methods on such a hybrid social-affiliation network.Experiments conducted on both synthetic and real datasets demonstrate theeffectiveness of our proposed methods.

Design of Efficient Sampling Methods on Hybrid Social-Affiliation Networks