  Online social network services provide a platform for human social
interactions. Nowadays, many kinds of online interactions generate large-scale
social network data. Network analysis helps to mine knowledge and pattern from
the relationship between actors inside the network. This approach is important
to support predictions and the decision-making process in many real-world
applications. The social network analysis methodology, which borrows approaches
from graph theory provides several metrics that enabled us to measure specific
properties of the networks. Some of the metrics calculations were built with no
scalability in minds, thus it is computationally expensive. In this paper, we
propose a graph sampling approach to reduce social network size, thus reducing
computation operations. The performance comparison between natural graph
sampling strategies using edge random sampling, node random sampling, and
random walks are presented on each selected graph property. We found that the
performance of graph sampling strategies depends on graph properties measured.
