  Networks represent relationships between entities in many complex systems,
spanning from online social interactions to biological cell development and
brain connectivity. In many cases, relationships between entities are
unambiguously known: are two users 'friends' in a social network? Do two
researchers collaborate on a published paper? Do two road segments in a
transportation system intersect? These are directly observable in the system in
question. In most cases, relationship between nodes are not directly observable
and must be inferred: does one gene regulate the expression of another? Do two
animals who physically co-locate have a social bond? Who infected whom in a
disease outbreak in a population?
  Existing approaches for inferring networks from data are found across many
application domains and use specialized knowledge to infer and measure the
quality of inferred network for a specific task or hypothesis. However, current
research lacks a rigorous methodology which employs standard statistical
validation on inferred models. In this survey, we examine (1) how network
representations are constructed from underlying data, (2) the variety of
questions and tasks on these representations over several domains, and (3)
validation strategies for measuring the inferred network's capability of
answering questions on the system of interest.
