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.

中文翻译：

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一种从合着网络和作者的科学领域确定研究组的新方法。

大型网络不仅有大量的顶点，而且还有大量的边。通常这样的网络密集且难以可视化，即使在本地也是如此。本文考虑了边上的大权重表示相应末端顶点的接近度的情况。我们在本文中遵循两个主要思想。第一个是\emph{网络修剪}，即去除边缘，使生成的网络更易于管理，同时保持原始网络的主要特征。另一个想法是以这样一种方式划分网络顶点集，即诱导连接的组件代表组合在一起的网络元素组。此外，我们假设网络的顶点由 \emph{types} 标记。在本文中，我们将我们的方法应用于斯洛文尼亚研究人员的合着网络，以确定研究小组、寻找小组领导和小组的跨学科程度。对于网络修剪阶段，我们使用探路者网络和顶点分区适当的线切割。每个集群都被分配了一个类型的分布。研究小组跨学科的衡量标准是从这样的分布中得出的。