Genealogical research is the tracing of an individual’s ancestral history using historical records, both official and unofficial. Challenges about genealogy problem like spelling names, legacy of a researcher can be measured not only in terms of his/her publications and scientific discoveries, in terms of the formation of other researchers. Now, research work is improving than oldest research. So population of researcher and scientist is increasing rapidly and it was more important now a days that to finding out who is better among all researcher. Author ranking can be solved this problem. Author ranking will not be perfect due to some causes, like naming disambiguation problem and uses of multiple name in paper. In Academic genealogy, is the relationship between advisor and advisee. Research area of advisor is more popular than his advisee research area may be good. From there we can do future prediction of an author. Another problem of author name disambiguity can be solved using genealogy tree hierarchy, as there are less chances of conflict in identifying an author based on his unique academic records. Another important challenge is that how much level (generation) we can visit from the genealogy tree. From the big dataset, we extract different metrics for an author. In this paper, we extract data of a particular author and from there we have analyze effects of an author rank.

Genealogy tree, Author name disambiguation, Citation

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