For the study of citation networks, a challenging problem is modeling the
high clustering. Existing studies indicate that the promising way to model the
high clustering is a copying strategy, i.e., a paper copies the references of
its neighbour as its own references. However, the line of models highly
underestimates the number of abundant triangles observed in real citation
networks and thus cannot well model the high clustering. In this paper, we
point out that the failure of existing models lies in that they do not capture
the connecting patterns among existing papers. By leveraging the knowledge
indicated by such connecting patterns, we further propose a new model for the
high clustering in citation networks. Experiments on two real world citation
networks, respectively from a special research area and a multidisciplinary
research area, demonstrate that our model can reproduce not only the power-law
degree distribution as traditional models but also the number of triangles, the
high clustering coefficient and the size distribution of co-citation clusters
as observed in these real networks