  Much of applied network analysis concerns with studying the existing
relationships between a set of agents; however, little focus has been given to
the considerations of how to represent observed phenomena as a network object.
In the case of physical structures such as electric grids or transportation
flows, the construction of a network model is fairly straightforward as the
nodes and edges usually correspond to some physical structure themselves. On
the other hand, construction of a social network is much less defined; while
nodes may correspond to well defined social agents, such as people or groups of
people, there much more liberty in defining the relationship that edges should
represent. This paper studies the intricacies of constructing a social network
from data, in particular, binary data, applicable to a wide range of social
science contexts. We examine several methods of constructing social networks in
prior literature and discuss the methods under a common framework. Finally,
using a data set of meetings among technologists in New York, we show that the
different constructions of social networks arising from various interpretations
of the underlying social relationships can result in vastly different network
structures. These findings highlight the significance of understanding the
precise relationships of interest when building a network model.
