Determining the Number of Clusters in Co-authorship Networks Using Social Network Theory

Spectral clustering is a modern data clustering methodology with many notable advantages. However, this method has a weakness in that it requires researchers to specify a priori the number of clusters. In most cases, it is a challenge to know the number of clusters accurately. Here, we propose a novel way to solve this problem by involving the concept of group leaders and members from social network theory. From the perspective of social networks, groups are organized by leaders and this can provide a hint to finding the number of clusters in social networks by identifying group leaders. However, due to the fact that a group can have more than one leader, we also propose an algorithm to combine leaders from the same group. The number of leaders after the combination is expected to be the number of clusters in a network. We validate this proposed approach by using spectral clustering to cluster data comprising the co-authorship network from the University of Technology, Sydney (UTS). The experimental results show that our proposed method is effective in determining the number of cluster and can facilitate spectral clustering to achieve better clusters compared with other methods of calculating the number of clusters.