Note 18 - A quote by Barabási

Albert-Laszlo Barabási at the World Economic Forum  2012

CC BY-SA 2.0: By World Economic Forum from Cologny, 

Switzerland - Mastering Complexity, 

https://commons.wikimedia.org/w/index.php?curid=21275699

Note 18 is simply a quote from an early article by Albert-László Barabási on how complex network analysis helps to tame complexity from 2005. I believe that we are still at the beginning of the journey, while---of course---the field has made serious progress on the analysis of, e.g., dynamic and multiplex networks:

Yet, the road to a fundamental and comprehensive understanding of networks is still rather rocky. (Barabási, 2005)

Reference:

Albert-László Barabási: "Taming complexity", Nature Physics, 1:68–70, 2005

Note 17: Universal features vs. contextual interpretation

The last point already made the point that some edge weights representing real-world concepts such as probabilities or friendship do not allow a meaningful interpretation of graph theoretic distances. Such an interpretation is depending on the context, the meaning of the relationships and weights in the real-world. However, hip new "network science" was in part so very hot in contrast to lame, old "social network analysis", because it just applied any kind of measure to all kinds of complex networks to identify structures common to all of them. This was the case for Watts' and Strogatz' seminal paper on Small-Worlds (Watts, 1998) or for Barabási and Albert's paper on Scale-Free Networks (Barabási, 1999).