2011/04/30 by Fuxin Ren, Fu-Xin Ren, Huawei Shen +3 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Advanced Clustering Algorithms Research #Artificial intelligence #Citation #Cluster analysis #Clustering coefficient #Complex Network Analysis Techniques #Complex network #Computer science #Copying #Data mining #Mathematics #Opinion Dynamics and Social Influence #Point (geometry) #Preferential attachment #World Wide Web #cs.DL #cs.SI #physics.soc-ph
paper · pdf · doi:10.1016/j.physa.2012.02.001
published as Physica A: Statistical Mechanics and its Applications, 391: 3533-3539, (2012)
openalex publication_date 2012/02/10 · arxiv created 2012/02/24 · arxiv updated 2015/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.