In this paper we perform a network based analysis of scientific collaboration networks. The analysis of the collaboration networks provides an insight into the quality of the relations among participants in the collaboration network. It may identify leaders and crucial participants in the network, domains of interest, closely related communities and future links. This is all of great importance for studying knowledge sharing among participants. We describe a set of network measures and algorithms chosen from the standard complex networks methodology, which are suitable for the research of scientific collaboration networks. The focus of this study has been placed on the scientific communities and linking. Communities can provide information about how collaboration is evolving over time. More precisely, the analysis of collaboration communities tells us about how well the participants are connected and how well they communicate. Next, the paper describes a case study in which the selected measures are applied to the collaboration networks that have emerged from STSMs (short-term scientific missions) on the KEYSTONE COST Action (semanticKEYword-based Search on sTructured data sOurcEs).