Abstract: Over the past decade, there has been an explosion of interest in
network research across the physical and social sciences. For social
scientists, the theory of networks has been a gold mine, yielding
explanations for social phenomena in a wide variety of disciplines from
psychology to economics. Here, we review the kinds of things that social
scientists have tried to explain using social network analysis and provide
a nutshell description of the basic assumptions, goals, and explanatory
mechanisms prevalent in the field. We hope to contribute to a dialogue
among researchers from across the physical and social sciences who share a
common interest in understanding the antecedents and consequences of
*  Network Analysis in the Social Sciences, Stephen P. Borgatti, Ajay
Mehra, Daniel J. Brass, Giuseppe Labianca, 2009/02/13, DOI:
10.1126/science.1165821, Science Vol. 323. no. 5916, pp. 892 – 895 
During the last three weeks, I have attended two different conferences – both focused entirely on (Social) Networks: First, I went to Greece to attend the International Conference for Social Network Analysts (main audience/attendance: social scientists) and I am currently blogging from the NetScience conference in New York in the Hall of Science (main audience: scientists).
I talked to a lot of people and listend to a lot of talks at both conferences and I noticed a couple of interesting things:
Researchers in all fields, natural and social sciences are working on (social) networks and within their specific fields they are located in a very specific niche within their own discipline. This is reflected for example in the fact, that a lot of researchers feel obligated to explain what a social network is and what the definition of concepts such as centrality are.
The basic concepts and analysis methods are the same across all disciplines, but we all use different language to describe what we are doing.
Researchers in different fields have different needs for analyzing and visualizing their network data and those who have the abilities to do so are creating/programming their own visualization and analysis tools or libraries. This seems to be an exploding area and I see a potential to synchronize the different needs and tools across disciplines.
Academic disciplines on (social) network research are largely disconnected and innovation is occurring within the disciplines, but usually not across disciplines. It seems as if the wheel is reinvented, but because academic disciplines are isolated and siloed the overall network science field is extremely innovative for its specific audiences.