Time Reversed Delay Differential Equation Based Modeling Of Journal Influence In An Emerging Area

TL;DR

This paper introduces a novel delay differential equation model, based on big data analysis, to explain the rapid rise in influence of a niche journal, ASCOM, by analyzing historical influence data and control variables.

Contribution

It presents a new modeling approach using delay differential equations combined with big data methods to understand journal influence dynamics.

Findings

Model successfully captures influence growth patterns.

Identifies key parameters affecting journal reputation.

Demonstrates the importance of historical data in influence modeling.

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 its niche prominence. We investigate the notable ascendancy in reputation of ASCOM by proposing a novel differential equation based modeling. The modeling is a consequence of knowledge discovery from big data methods, namely L1-SVD. 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 analyzing the cause and control variables behind rapid rise in the reputation of a journal in a niche area. We intend to identify and probe the 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. The manuscript justifies the use of implicit control variables and models those accordingly that demonstrate certain behavior in the journal influence.