  The increasing interest in complex networks research has been a consequence
of several intrinsic features of this area, such as the generality of the
approach to represent and model virtually any discrete system, and the
incorporation of concepts and methods deriving from many areas, from
statistical physics to sociology, which are often used in an independent way.
Yet, for this same reason, it would be desirable to integrate these various
aspects into a more coherent and organic framework, which would imply in
several benefits normally allowed by the systematization in science, including
the identification of new types of problems and the cross-fertilization between
fields. More specifically, the identification of the main areas to which the
concepts frequently used in complex networks can be applied paves the way to
adopting and applying a larger set of concepts and methods deriving from those
respective areas. Among the several areas that have been used in complex
networks research, pattern recognition, optimization, linear algebra, and time
series analysis seem to play a more basic and recurrent role. In the present
manuscript, we propose a systematic way to integrate the concepts from these
diverse areas regarding complex networks research. In order to do so, we start
by grouping the multidisciplinary concepts into three main groups, namely
features, similarity, and network connectivity. Then we show that several of
the analysis and modeling approaches to complex networks can be thought as a
composition of maps between these three groups, with emphasis on nine main
types of mappings, which are presented and illustrated. Such a systematization
of principles and approaches also provides an opportunity to review some of the
most closely related works in the literature, which is also developed in this
article.
