Over the past few decades, scientific collaboration has been widely considered an important driver of research innovation. By collaborating together, scientists can benefit from both methodological and technological complementarities and synergy, improving the quality and quantity of their research outputs. As evidence of this, collaboration among scientists is increasing in all disciplines and government policies in international exchange programs are aimed at promoting collaboration among researchers. Collaboration among scientists can be represented as a network, usually adopting co-authorship as linkages. In this view, Social Network Analysis provides a useful theoretical and methodological approach because collaboration features can be related to the topological characteristics of the network. Recently, several empirical studies have found positive associations between researchers’ position in the co-authorship network and their productivity, although the results can be different depending on the discipline, scientific performance measure, and data source retrieved to construct the co-authorship networks. In this contribution, we propose the use of SNA tools for scientific evaluation purposes. Network indices at the individual and subgroup levels will be introduced to analyze the relation with both the individual research productivity and scientific output quality measure provided by the Italian academic researchers involved in VQR from the period 2011–2014.
