  Social networks play a key role in studying various individual and social
behaviors. To use social networks in a study, their structural properties must
be measured. For offline social networks, the conventional procedure is
surveying/interviewing a set of randomly-selected respondents. In many
practical applications, inferring the network structure via sampling is too
prohibitively costly. There are also applications in which it simply fails. For
example, for optimal vaccination or employing influential spreaders for public
health interventions, we need to efficiently and quickly target well-connected
individuals, which random sampling does not accomplish. In a few studies, an
alternative sampling scheme (which we dub `alter sampling') has proven useful.
This method simply targets randomly-chosen neighbors of the randomly-selected
respondents. A natural question that arises is: to what extent does this method
generalize? Is the method suitable for every social network or only the very
few ones considered so far? In this paper, we demonstrate the robustness of
this method across a wide range of networks with diverse structural properties.
The method outperforms random sampling by a large margin for a vast majority of
cases. We then propose an estimator to assess the advantage of choosing alter
sampling over random sampling in practical scenarios, and demonstrate its
accuracy via Monte Carlo simulations on diverse synthetic networks.
