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Large networks not only have a large number of vertices but also have a large number of edges. Usually such networks are dense and difficult to visualise, even locally. This paper considers the case where large weights on edges represent proximity of the corresponding end-vertices. We follow two main ideas in this paper. The first one is \emph{network pruning}, that is removal of edges that makes the resulting network more manageable while keeping the main characteristic of the original network. The other idea is to partition the network vertex set in such a way that the induced connected components represent groups of network elements that fit together. Furthermore, we assume that the vertices of the network are labeled by \emph{types}. In this paper we apply our approach to co-authorship network of researchers in Slovenia in order to identify research groups, finding group leaders and the degree of inter-disciplinarity of the group. For the network pruning phase we use a pathfinder network and for vertex partition appropriate line-cuts. Each cluster is assigned a distribution of types. A measure of inter-disciplinarity of research group is derived from such a distribution.

A novel method for determining research groups from co-authorship network and scientific fields of authors.