  Social network based applications have experienced exponential growth in
recent years. One of the reasons for this rise is that this application domain
offers a particularly fertile place to test and develop the most advanced
computational techniques to extract valuable information from the Web. The main
contribution of this work is three-fold: (1) we provide an up-to-date
literature review of the state of the art on social network analysis (SNA);(2)
we propose a set of new metrics based on four essential features (or
dimensions) in SNA; (3) finally, we provide a quantitative analysis of a set of
popular SNA tools and frameworks. We have also performed a scientometric study
to detect the most active research areas and application domains in this area.
This work proposes the definition of four different dimensions, namely Pattern
& Knowledge discovery, Information Fusion & Integration, Scalability, and
Visualization, which are used to define a set of new metrics (termed degrees)
in order to evaluate the different software tools and frameworks of SNA (a set
of 20 SNA-software tools are analyzed and ranked following previous metrics).
These dimensions, together with the defined degrees, allow evaluating and
measure the maturity of social network technologies, looking for both a
quantitative assessment of them, as to shed light to the challenges and future
trends in this active area.
