Modeling the clustering in citation networks

TL;DR

This paper introduces a new citation network model that better captures high clustering by incorporating connecting patterns among papers, successfully reproducing key network properties observed in real data.

Contribution

A novel model for citation networks that accurately reproduces high clustering and triangle counts by leveraging connecting patterns among papers.

Findings

The model reproduces the power-law degree distribution.

It accurately predicts the number of triangles and clustering coefficients.

It matches the size distribution of co-citation clusters.

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.