Abstract
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.
Highlights
In contemporary research community scientists collaborate within formal or informal research groups
In this note we propose a method that uses the co-authorship network on the one hand and declared scientific field of authors that can be extracted from some bibliographic databases, on the other
In this paper we apply it to co-authorship networks
Summary
In contemporary research community scientists collaborate within formal or informal research groups. In this note we propose a method that uses the co-authorship network on the one hand and declared scientific field of authors that can be extracted from some bibliographic databases, on the other. 140 Informatica 44 (2020) 139–145 the number of edges and increase the number of components, in our case producing research groups. Note that the idea of using MST, pathfinder and MST-pathfinder has been used extensively in the past in variety of contexts of bibliographic and other research[6, 8, 11, 22, 23] This rough general approach may be refined in several different ways.
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