  Analyzing social graphs with limited data access is challenging for
third-party researchers. To address this challenge, a number of algorithms that
estimate structural properties via a random walk have been developed. However,
most existing algorithms are limited to the estimation of local structural
properties. Here we propose a method for restoring the original social graph
from the small sample obtained by a random walk. The proposed method generates
a graph that preserves the estimates of local structural properties and the
structure of the subgraph sampled by a random walk. We compare the proposed
method with subgraph sampling using a crawling method and the existing method
for generating a graph that structurally resembles the original graph via a
random walk. Our experimental results show that the proposed method more
accurately reproduces the local and global structural properties on average and
the visual representation of the original graph than the compared methods. We
expect that our method will lead to exhaustive analyses of social graphs with
limited data access.
