The paper investigates the impact of clusters, or localized networks involving industrial, academic and institutional players, in the life-science setting and aims to enrich the line of inquiry into cluster-based innovation by applying a social network analysis (SNA) approach. The cluster concept has been defined in ambiguous ways, corresponding to a large variety of spatial and organizational concrete configurations. We try to understand which of these configurations - i.e. what structural and nodal network characteristics of the cluster - are best suited to maximize the likelihood of clusters' innovation, from an intra-cluster and intercluster perspective. Quantitative methods are applied to relational and nodal data, using SNA and a regression model. The work sheds light on the factors that give rise to differential innovative outcomes across different clusters.