The Aging Effect in Evolving Scientific Citation Networks

TL;DR

This paper introduces a hypergraph-based evolutionary model incorporating aging effects to better understand the temporal dynamics of scientific citation networks, validated with real datasets from physics and computer science.

Contribution

It presents a novel hypergraph model with aging effects for citation networks, providing theoretical solutions and empirical validation across disciplines.

Findings

Early publications' attraction decays exponentially.

Aging significantly influences citation pattern modeling.

The model improves understanding of scientific evolution mechanisms.

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.