Social networks are dynamic social structures consisting of individuals or organizations, usually represented by nodes tied by one or more relationship type. Analyzing these structures enables us to detect several inter and intra connections between people in and outside their organizations. In this context, we construct a multi-relational scientific social network where researchers may have four different types of relationships with each other. We adopt some criteria such as relationship age in order to assign a weight to relationships and 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.

knowledge flow, Multi-relational Scientific Social Network Analysis, Max Flow Grouping Algorithm, weighted relationships