Reference-Based Publication Networks with Episodic Memories

TL;DR

This study explores how the topology of reference-based publication networks varies with the age of references, revealing a transition from scale-free to Gaussian degree distributions as references age increases.

Contribution

It introduces a method to analyze publication networks based on reference age, showing how network topology evolves with reference aging, which is a novel approach.

Findings

Network topology depends on reference age.

All references produce a scale-free network.

Older references lead to Gaussian degree distribution.

Abstract

In this paper we report first results of our study on network characteristics of a reference-based, bibliographically coupled (BC) publication network structure. We find that this network of clustered publications shows different topologies depending on the age of the references used for building the network. A remarkable finding is that only the network structure based on all references within publications is characterized by a degree distribution with a power-law dependence. This topology, which is typical for scale-free networks, disappears when selecting references of a specific age for the clustering process. Structuring the publication network as a function of reference age, allows 'tuning through the episodic memory' of the nodes of the network. We find that the older the references, the more the network tends to change its topology towards a Gaussian degree distribution.