  Big Data has become the primary source of understanding the structure and
dynamics of the society at large scale. The network of social interactions can
be considered as a multiplex, where each layer corresponds to one communication
channel and the aggregate of all of them constitutes the entire social network.
However, usually one has information only about one of the channels or even a
part of it, which should be considered as a subset or sample of the whole. Here
we introduce a model based on a natural bilateral communication channel
selection mechanism, which for one channel leads to consistent changes in the
network properties. For example, while it is expected that the degree
distribution of the whole social network has a maximum at a value larger than
one, we get a monotonously decreasing distribution as observed in empirical
studies of single channel data. We also find that assortativity may occur or
get strengthened due to the sampling method. We analyze the far-reaching
consequences of our findings.
