Modeling the clustering in citation networks

Fu-Xin Ren, Hua-Wei Shen and Xue-Qi Cheng

Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, issue 12, 3533-3539

Abstract: 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 neighbor 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.

Keywords: Citation network modeling; High clustering; Triangle number; Growth model (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:391:y:2012:i:12:p:3533-3539

DOI: 10.1016/j.physa.2012.02.001

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