  As social network analysis (SNA) has drawn much attention in recent years,
one bottleneck of SNA is these network data are too massive to handle.
Furthermore, some network data are not accessible due to privacy problems.
Therefore, we have to develop sampling methods to draw representative sample
graphs from the population graph. In this paper, Metropolis-Hastings Random
Walk (MHRW) and Random Walk with Jumps (RWwJ) sampling strategies are
introduced, including the procedure of collecting nodes, the underlying
mathematical theory, and corresponding estimators. We compared our methods and
existing research outcomes and found that MHRW performs better when estimating
degree distribution (61% less error than RWwJ) and graph order (0.69% less
error than RWwJ), while RWwJ estimates follower and following ratio average and
mutual relationship proportion in adjacent relationship with better results,
with 13% less error and 6% less error than MHRW. We analyze the reasons for the
outcomes and give possible future work directions.
