Abstract
The study of citation networks is of interest to the scientific community. However, the underlying mechanism driving individual citation behavior remains imperfectly understood, despite the recent proliferation of quantitative research methods. Traditional network models normally use graph theory to consider articles as nodes and citations as pairwise relationships between them. In this paper, we propose an alternative evolutionary model based on hypergraph theory in which one hyperedge can have an arbitrary number of nodes, combined with an aging effect to reflect the temporal dynamics of scientific citation behavior. Both theoretical approximate solution and simulation analysis of the model are developed and validated using two benchmark datasets from different disciplines, i.e. publications of the American Physical Society (APS) and the Digital Bibliography & Library Project (DBLP). Further analysis indicates that the attraction of early publications will decay exponentially. Moreover, the experimental results show that the aging effect indeed has a significant influence on the description of collective citation patterns. Shedding light on the complex dynamics driving these mechanisms facilitates the understanding of the laws governing scientific evolution and the quantitative evaluation of scientific outputs.
Highlights
IntroductionScientific citation networks have provided a versatile and efficient tool to understand the structure and evolution of scientific progress (Martin et al 2013; Wang et al 2013; Leydes‐ dorff 1998; Cronin 1984; Liu et al 2018; Shi et al 2019) by depicting topological interac‐ tions between academic publications and the propagation of scientific memes (Kuhn et al 2014; Strogatz 2001; Shen et al 2014), facilitating the emergence of a new research para‐ digm, the science of science (Fortunato et al 2018; Zeng et al 2017; Niu et al 2016)
We have studied aging effect on the evolution of hypergraph-based citation networks
Empirical analyses from two widely used datasets, American Physical Society (APS) and Digital Bibliography & Library Project (DBLP) publications, show that the hyperdegree distribution is significantly affected by the aging factor
Summary
Scientific citation networks have provided a versatile and efficient tool to understand the structure and evolution of scientific progress (Martin et al 2013; Wang et al 2013; Leydes‐ dorff 1998; Cronin 1984; Liu et al 2018; Shi et al 2019) by depicting topological interac‐ tions between academic publications and the propagation of scientific memes (Kuhn et al 2014; Strogatz 2001; Shen et al 2014), facilitating the emergence of a new research para‐ digm, the science of science (Fortunato et al 2018; Zeng et al 2017; Niu et al 2016). One would expect early publica‐ tions in a particular field to be highly cited, showing a strong first-mover effect (Newman 2009). Studies are more likely to cite newly published papers to sur‐ vey recent advances (Wei et al 2013; Redner 1998; Leicht et al 2007), exerting a strong impact on the shape of the citation distribution (Newman 2014; Dorogovtsev and Mendes 2000). The temporal effect plays a significant role in modeling growing citation networks (Dorogovtsev and Mendes 2002; Medo et al 2011)
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