Genealogy tree: understanding academic lineage of authors via algorithmic and visual analysis

TL;DR

This paper explores the use of genealogy trees to analyze academic lineage and networks among researchers, utilizing visualization tools and citation analysis to understand influence, collaboration, and community formation.

Contribution

It introduces a novel approach to understanding academic relationships through genealogy trees and visual analysis, highlighting their role in research influence and community detection.

Findings

Academic genealogy helps in understanding researcher influence.

Researchers tend to collaborate within their academic circles.

Genealogy analysis can detect research communities and improve metrics.

Abstract

Ancestry and genealogy tree are proven tools to determine the lineage of any person and establish dependencies among individuals. Genealogy tree can be exploited further to gain information about the researcher and his scholastic lineage which is of paramount importance in today's world of computer technology. this insight into academic genealogy could be a way of helping PHD students achieve academic socialization within the discipline, by making explicit connections that may be influential. Awareness of his scientific heritage gives the user a broader perspective of his own research project. This paper also highlights and investigates how this academic network is exploited by certain researchers using various visualization tools. It was observed during this work that the credibility and influence factor is determined by the various citations obtained by an author and to improve their rankings in various forms, they tend to collaborate in their academic circle and boost their citation count. A recent trend among researchers is to form communities based on their academic relationships and rely on copious citations for their mutual benefit. Tracing the genealogical relationships can be helpful in detecting such communities and also create a more quality aware metric using a lineage independent model for computation of author level metrics.