The Simultaneous Evolution of Author and Paper Networks

TL;DR

This paper introduces a comprehensive model that simulates the co-evolution of author and paper networks, validated against real data, revealing insights into scientific citation behaviors and network structures.

Contribution

It presents a novel process model, TARL, that captures the joint growth of author and citation networks incorporating topics, aging, and recursive linking mechanisms.

Findings

The model accurately reproduces the network properties observed in real data.

Citation patterns are influenced by topics, recency bias, and recursive citing behaviors.

The number of topics correlates with the clustering coefficient of the citation network.

Abstract

There has been a long history of research into the structure and evolution of mankind's scientific endeavor. However, recent progress in applying the tools of science to understand science itself has been unprecedented because only recently has there been access to high-volume and high-quality data sets of scientific output (e.g., publications, patents, grants), as well as computers and algorithms capable of handling this enormous stream of data. This paper reviews major work on models that aim to capture and recreate the structure and dynamics of scientific evolution. We then introduce a general process model that simultaneously grows co-author and paper-citation networks. The statistical and dynamic properties of the networks generated by this model are validated against a 20-year data set of articles published in the Proceedings of the National Academy of Science. Systematic deviations from a power law distribution of citations to papers are well fit by a model that incorporates a partitioning of authors and papers into topics, a bias for authors to cite recent papers, and a tendency for authors to cite papers cited by papers that they have read. In this TARL model (for Topics, Aging, and Recursive Linking), the number of topics is linearly related to the clustering coefficient of the simulated paper citation network.