Modeling the clustering in citation networks

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 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.

References in corpus (7)

• Uncovering the overlapping community structure of complex networks in nature and society
• Detect overlapping and hierarchical community structure in networks
• Emergence of communities in weighted networks
• Characterizing and modeling citation dynamics
• Quantifying and identifying the overlapping community structure in networks
• Modeling scientific-citation patterns and other triangle-rich acyclic networks
• Triangular clustering in document networks

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• Modeling citation networks based on vigorousness and dormancy
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• Associations between author-level metrics in subsequent time periods
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