Network analysis - is the 'divide' between social and natural scientists detrimental, fruitful or just a nuisance?

The story goes that social network analysis, and network analysis more generally, was first conceived as a scientific endeavor within the social sciences in the 1930's (Moreno [and Jennings] 1934), and that it much later in the 1990's became a subject of interest to the natural sciences (e.g. Barabasi 2003). There has been discussions during the year as wether the natural scientists have just re-disovered what sociologists already had developed, and that they have done so without giving tribute to the social scientists that spearheaded this network analytic approach to practice science (see Borgatti et al. 2009 for balance account from the social science viewpoint).

On this note, I just came across this very interesting blogpost by Brian Keegan that had visited a very excited event where some of the very big names within network analysis from both these disciplines met, engaged, and discussed. I recommend reading his whole blogpost "Report from ANN/SONIC/NICO conferences". Extracts from it reads: 

The Annenberg Network of Networks, Northwestern SONIC lab, and Northwestern Institute on Complex Systems (NICO) just concluded back-to-back conferences that assembled some truly intimidating intellectual wattage into a single room; Adamic, Amaral, Barabasi, Berners-Lee, Burt, Castells, Macy, Newman, Poole, Powell, Uzzi, Wasserman, and Watts were just some of the luminaries present and speaking.
All of the talks were provocative, but Stan Wasserman best fulfilled the role of the Socratic gadfly by giving the most pointed talk with respect to both the issues he raised and the conversation that ensued. [...] In the discussion that followed Stan's talk, both Duncan Watts and Brian Uzzi raised the point that this inward facing orientation is endemic to all academic disciplines: psychologists independently replicating sociological findings are nevertheless cited by other psychologists, for example. [...] 
Brian goes on to make some of his own arguments based on his listening, of which some are:

However, I thought that Stan made far too narrow a point. Methodological congruity is certainly something all fields should aspire towards, but what is at stake is not only the inefficiencies of re-inventing methodological wheels, but also the coherence of an emerging field that revels in its interdisciplinary mantle while in practice appearing to shun any substantive literacy.
Should network science then be a field unto itself, a tradition manifesting itself within many distinct disciplines, an interface/hyperedge between disciplines, or something else altogether? Perhaps network scientists should look especially to the history of statistics for some lessons on how to best navigate the rapids of interdisciplinary.
He finds some kind of balance in Ron Burt and Merton:
Ron Burt made the persuasive case that preoccupation with elucidating the work one's forebears across all fields may impose constraints on the progress of the field and multiple discoveries provide opportunities for recombination of resources. Indeed, were we to return Merton on exactly this topic, he states that "[multiple discoveries] become virtually inevitable when prerequisite kinds of knowledge and tools accumulate in man's cultural store and when the attention of an appreciable number of investigators becomes focused on a problem, by emerging social needs, by developments internal to the science, or by both." (1963, European J. of Sociology, 4: 237-282.) 
As already stated, read the whole blog from Brian Keegan and follow his blog maybe.


Borgatti, S.P., Mehra, A., Brass, D.J. and Labianca, G. (2009). Network analysis in the social sciences. Science, 323: 892-95.

Moreno, J. (1934). Who Shall Survive?  Washington DC: Nervous and Mental Disease Publishing Company.

Barabási, A.-L. (2003). Scale-free networks. Scientific American, 288: 60-69.