Scale-free identity: The emergence of social network science

Abstract

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.