Group and link analysis of multi-relational scientific social networks

Analyzing social networks enables us to detect several inter and intra connections between people in and outside their organizations. We model a multi-relational scientific social network where researchers may have four different types of relationships with each other. We adopt some criteria to enable the modeling of a scientific social network as close as possible to reality. Using clustering techniques with maximum flow measure, we identify the social structure and research communities in a way that allows us to evaluate the knowledge flow in the Brazilian scientific community. Finally, we evaluate the temporal evolution of scientific social networks to suggest/predict new relationships.

► We propose a model to study multi-relational scientific social network. This model can be applied to any organization, whether scientific or not. ► We use a Max-flow grouping algorithm to identify the social structure and research communities. ► We evaluate the knowledge flow in the Brazilian scientific community. ► We compare the main differences in social structure between homogeneous and multi-relational social networks. ► We propose a new metric to evaluate the temporal evolution of scientific social networks to suggest/predict new relationships.