Abstract

Scientific domains are fluid entities that change and turn as time passes. Take machine learning as an example. Up until the’90s, most of the methods were expert-knowledge-driven. However, as time passed, more data-driven approaches appeared, finally leading to the advent of deep learning methods. As a result, in a span of 30 years, the field has gone through many changes and breakthroughs and is at a point where many novelties have a life span of shorter than five years. In parallel, a regular researcher’s career span is around the same length. Consequently, being a researcher requires shifts in the field of study throughout one’s career. Besides, researchers’ scientific interests are inherently dynamic and change over time. Hence, there exists a dynamicity to authors’ interests and fields of work over time. In this work, we study this phenomenon through systematic approaches for representing and tracking dynamicity in different epochs. Our representation approaches are based on the idea that each author could be represented as a distribution of other authors. Concurrently, our tracking approaches rely on established mathematical concepts for measuring the change between two distributions. We focus on the publications in the 2001-2020 range and present a set of analyses built on top of the introduced approaches to understanding the potential connection between dynamicity and success.

Date

February 14, 2023

Authors

Richard Delwin Myloth, Kian Ahrabian, Arun Baalaaji Sankar Ananthan, Xinwei Du, Jay Pujara

Conference

SDU Workshop @ AAAI 2023