  Graph sampling via crawling has become increasingly popular and important in
the study of measuring various characteristics of large scale complex networks.
While powerful, it is known to be challenging when the graph is loosely
connected or disconnected which slows down the convergence of random walks and
can 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 is
related to an auxiliary graph and an affiliation graph, and the target graph
becomes well connected when we view it from the perspective of these three
graphs together, or called a hybrid social-affiliation graph in this paper.
When directly sampling the target graph is difficult or inefficient, we can
indirectly 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 the
effectiveness of our proposed methods.
