Learning Scholarly Network of Influential Authors

Abstract

There have previously been several algorithms developed for academic networks to determine the author's productivity and influence. The primary goal of these algorithms is to compute bibliometric characteristics like as publication and citation counts. There are some that are similar to the h-index, I-index, and G-Index. However, all of these are primarily concerned with citation and publication counts, and they have certain drawbacks as well. All of these algorithms are primarily used to determine the productivity and influence of authors. They most important factor which these algorithms lack is identify the contribution of an author in a research paper. Our purpose is to create author’s network using the available dataset (DBLP dataset) and then build position-based algorithm which take care of author position in research paper to find out the author actual productivity in related field. Base line of this algorithm will be h-index. To calculate the points author, gain due to his/her position in paper we will consider only that paper of an author which fall inside his h-index range.