  Social Network Analysis is a way of studying agents embedded in contexts. In
about 1998, physicists discovered social networks as representations of complex
systems. Small-world and scale-free networks are the paradigmatic models of
this Network Science. Relying on various models and mechanisms of
socio-cultural processes, an identity model is developed and calibrated in a
case study of Social Network Science. This research domain results from the
union of Social Network Analysis and Network Science. A unique dataset of
25,760 scholarly articles from one century of research (1916-2012) is created.
Clustering this set of publications, five subdomains are detected and analyzed
in terms of authorship, citation, and word usage structures and dynamics. The
scaling hypothesis of percolation theory is formulated for socio-cultural
systems, namely that power-law size distributions like Lotka's, Bradford's, and
Zipf's Law mean that the described identity resides at the phase transition
between the stability and change of meaning. In this case, it can be diagnosed
using bivariate scaling laws and Abbott's heuristic of fractal distinctions.
Identities are not dichotomies but dualities of social network and cultural
domain, micro and macro phenomena, as well as stability and change. Story sets
that give direction to research fluctuate less, are less distinctive, and more
inert than the individuals doing the research. Identities are scale-free. Six
senses are diagnostic of different aspects of identity, and when they come
together as process, a complex socio-cultural system comes into existence. A
mutual benefit that results from mating Relational Sociology and Network
Science is identified. The latter can learn from the former that social systems
are dualities of transactions and meaning. For the social sciences, the
importance of Paretian thinking (scale invariance) is pointed out.
