Model Visualization in understanding rapid growth of a journal in an emerging area

TL;DR

This paper introduces a differential equation-based model to understand the rapid growth of the journal Astronomy and Computing, using big data methods and historical influence factors to explain its rising reputation in a niche area.

Contribution

It presents a novel growth model incorporating delay differential equations and big data analysis to explain the journal's ascendancy, addressing limitations of previous ranking methods.

Findings

The model successfully captures the influence dynamics over time.

Parameters identified as key drivers of reputation growth.

Delay effects are significant in modeling influence changes.

Abstract

A recent independent study resulted in a ranking system which ranked Astronomy and Computing (ASCOM) much higher than most of the older journals highlighting the niche prominence of the particular journal. We investigate the remarkable ascendancy in reputation of ASCOM by proposing a novel differential equation based modeling. The Modeling is a consequence of knowledge discovery from big data-centric methods, namely L1-SVD. The inadequacy of the ranking method in explaining the reason behind the growth in reputation of ASCOM is reasonable to understand given that the study was post-facto. Thus, we propose a growth model by accounting for the behavior of parameters that contribute to the growth of a field. It is worthwhile to spend some time in analysing the cause and control variables behind rapid rise in reputation of a journal in a niche area. We intent to probe and bring out parameters responsible for its growing influence. Delay differential equations are used to model the change of influence on a journal's status by exploiting the effects of historical data.